Tutorial: Inferring enhancer-GRNs from multiome data with SCENIC+

[ ]:

%matplotlib inline
import scenicplus
scenicplus.__version__

In this tutorial we describe the minimum steps to generate a SCENIC+ object and build e-GRNs. Detailed tutorials for exploring the results will be provided.

1. Create SCENIC+ object

For generating a SCENIC+ you will require: * scRNA-seq annData object (e.g. scanpy) * scATAC-seq cisTopic object * Pycistarget motif enrichment dictionary

[1]:

# Load functions
from scenicplus.scenicplus_class import SCENICPLUS, create_SCENICPLUS_object
from scenicplus.preprocessing.filtering import *

First we will load the scRNA-seq and the scATAC-seq data. We make sure that names match between them.

[2]:

# Load data
## ATAC - cisTopic object
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/atac/pycistopic/10x_multiome_brain_cisTopicObject_noDBL.pkl', 'rb')
cistopic_obj = pickle.load(infile)
infile.close()
## Precomputed imputed data
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/atac/pycistopic/DARs/Imputed_accessibility.pkl', 'rb')
imputed_acc_obj = pickle.load(infile)
infile.close()
## RNA - Create Anndata
from loomxpy.loomxpy import SCopeLoom
from pycisTopic.loom import *
import itertools
import anndata
path_to_loom = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/rna/seurat/10x_multiome_brain_Seurat.loom'
loom = SCopeLoom.read_loom(path_to_loom)
metadata = get_metadata(loom)
# Fix names
metadata['barcode'] = [x.split('-')[0] for x in metadata.index.tolist()]
metadata['barcode'] = metadata['barcode'] + '-1'
metadata.index = metadata['barcode'] + '-10x_multiome_brain'
expr_mat = loom.ex_mtx
expr_mat.index = metadata['barcode'] + '-10x_multiome_brain'
rna_anndata = anndata.AnnData(X=expr_mat)
rna_anndata.obs = metadata

If you have generated your cisTopic object with an old version of pycisTopic, it is possible that your region data was affected by a previous bug. You can fix it with the code below:

[3]:

# Fix region data (bug in old pycistopic versions)
from pycisTopic.utils import region_names_to_coordinates
fragment_matrix = cistopic_obj.fragment_matrix
binary_matrix = cistopic_obj.binary_matrix
region_data = region_names_to_coordinates(cistopic_obj.region_names)
region_data['Width'] = abs(region_data.End -region_data.Start).astype(np.int32)
region_data['cisTopic_nr_frag'] = np.array(
fragment_matrix.sum(axis=1)).flatten()
region_data['cisTopic_log_nr_frag'] = np.log10(
region_data['cisTopic_nr_frag'])
region_data['cisTopic_nr_acc'] = np.array(
binary_matrix.sum(axis=1)).flatten()
region_data['cisTopic_log_nr_acc'] = np.log10(
region_data['cisTopic_nr_acc'])
cistopic_obj.region_data = region_data

Next we load the motif enrichment results into a dictionary. We can load motif results from the different methods in pycistarget (e.g. cisTarget, DEM) and different region sets (e.g. topics, DARs, MACS bdgdiff peaks). In this tutorial we will use both cisTarget and DEM peaks from topics and DARs.

[4]:

# Load cistarget and DEM motif enrichment results
motif_enrichment_dict={}
import pickle
from pycistarget.motif_enrichment_dem import *
path = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/atac/pycistarget/'
infile = open(path +'topics/topic_cistarget_dict.pkl', 'rb')
motif_enrichment_dict['CTX_Topics_All'] = pickle.load(infile)
infile.close()
infile = open(path +'topics/topic_DEM_dict.pkl', 'rb')
motif_enrichment_dict['DEM_Topics_All'] = pickle.load(infile)
infile.close()
infile = open(path +'DARs/DARs_cistarget_dict.pkl', 'rb')
motif_enrichment_dict['CTX_DARs_All'] = pickle.load(infile)
infile.close()
infile = open(path +'DARs/DARs_DEM_dict.pkl', 'rb')
motif_enrichment_dict['DEM_DARs_All'] = pickle.load(infile)
infile.close()

Now we can create the SCENIC+ object:

[5]:

scplus_obj = create_SCENICPLUS_object(
        GEX_anndata = rna_anndata,
        cisTopic_obj = cistopic_obj,
        imputed_acc_obj = imputed_acc_obj,
        menr = motif_enrichment_dict,
        ACC_prefix = 'ACC_',
        GEX_prefix = 'GEX_',
        bc_transform_func = lambda x: x,
        normalize_imputed_acc = False)

[6]:

print(scplus_obj)

SCENIC+ object with n_cells x n_genes = 1736 x 26399 and n_cells x n_regions = 1736 x 422146
        metadata_regions:'Chromosome', 'Start', 'End', 'Width', 'cisTopic_nr_frag', 'cisTopic_log_nr_frag', 'cisTopic_nr_acc', 'cisTopic_log_nr_acc'
        metadata_cell:'GEX_VSN_cell_type', 'GEX_VSN_leiden_res0.3', 'GEX_VSN_leiden_res0.6', 'GEX_VSN_leiden_res0.9', 'GEX_VSN_leiden_res1.2', 'GEX_VSN_sample_id', 'GEX_Seurat_leiden_res0.6', 'GEX_Seurat_leiden_res1.2', 'GEX_Seurat_cell_type', 'GEX_barcode', 'ACC_cisTopic_nr_frag', 'ACC_cisTopic_log_nr_acc', 'ACC_barcode', 'ACC_VSN_leiden_res0.6', 'ACC_FRIP', 'ACC_TSS_enrichment', 'ACC_cisTopic_nr_acc', 'ACC_VSN_leiden_res0.9', 'ACC_VSN_RNA+ATAC_leiden_100_2', 'ACC_Dupl_rate', 'ACC_sample_id', 'ACC_pycisTopic_leiden_10_0.6', 'ACC_pycisTopic_leiden_10_1.2', 'ACC_Seurat_leiden_res0.6', 'ACC_Total_nr_frag', 'ACC_Total_nr_frag_in_regions', 'ACC_VSN_leiden_res1.2', 'ACC_VSN_cell_type', 'ACC_Unique_nr_frag_in_regions', 'ACC_Seurat_cell_type', 'ACC_Predicted_doublets_fragments', 'ACC_Dupl_nr_frag', 'ACC_cisTopic_log_nr_frag', 'ACC_VSN_leiden_res0.3', 'ACC_Log_total_nr_frag', 'ACC_Unique_nr_frag', 'ACC_Doublet_scores_fragments', 'ACC_VSN_sample_id', 'ACC_Log_unique_nr_frag', 'ACC_Seurat_leiden_res1.2', 'ACC_Seurat_RNA+ATAC_leiden_100_2', 'ACC_pycisTopic_ingest_cell_type', 'ACC_pycisTopic_harmony_cell_type', 'ACC_pycisTopic_bbknn_cell_type', 'ACC_pycisTopic_scanorama_cell_type', 'ACC_pycisTopic_cca_cell_type'
        menr:'CTX_Topics_All', 'DEM_Topics_All', 'CTX_DARs_All', 'DEM_DARs_All'
        dr_cell:'ACC_UMAP', 'ACC_tSNE', 'ACC_VSN_RNA+ATAC_UMAP', 'ACC_Seurat_RNA+ATAC_UMAP'

You can also filter low accessible regions and low expressed genes. This recommended to avoid getting false relationships with these regions and genes.

[7]:

filter_genes(scplus_obj, min_pct = 0.5)
filter_regions(scplus_obj, min_pct = 0.5)

2022-01-04 17:24:16,750 Preprocessing INFO     Going from 26399 genes to 20448 genes.
2022-01-04 17:24:50,192 Preprocessing INFO     Going from 422146 regions to 375805 regions.

2. Generate cistromes

The next step is to generate cistromes. By default, all targets assigned to a TF across the motif enrichment dictionaries will be taken, and overlapped with regions in the SCENIC+ object. However, it is possible to also subset for regions accessible in certain cell type as well, to generate cell type specific cistromes. This approach is described in the tutorial cistrome_pruning_advanced.ipynb.

[8]:

# Merge cistromes (all)
from scenicplus.cistromes import *
import time
start_time = time.time()
merge_cistromes(scplus_obj)
time = time.time()-start_time
print(time/60)

7.251678760846456

3. Optional: Assess TF-cistrome correlation

In addition, cistromes can be pruned based on the correlarion between the presence of a TF and the accessibility of the cistrome. SCENIC+ can be run without this pruning step, deriving eGRNs for each candidate TF, but the assesment of the TF-cistrome correlation can help to prioritize TFs and high confidence GRNs.

A. Score cistromes in cells

The first step is to score the cistromes in each cell. For this purpose, we will use AUCell, using cell rankings based on region accessibility.

[9]:

# Generate AUC ranking
import time
start_time = time.time()
ranking = make_rankings(scplus_obj)
time = time.time()-start_time
print(time/60)

2.333057375748952

[10]:

# Get cistrome enrichment
import time
start_time = time.time()
score_cistromes(scplus_obj,
                ranking,
                cistromes_key = 'Unfiltered',
                enrichment_type = 'region',
                auc_threshold = 0.05,
                normalize = False,
                n_cpu = 8)
time = time.time()-start_time
print(time/60)

4.658317136764526

B. Generate pseudobulks

Due to the amount of drop-outs, and the variability in cell types proportions, using directly the AUC cistrome matrix can result in noisy correlations. Here, we use pseudobulks, in which we sample a number of cells per cell type. In this example, we merge 5 cells per pseudobulk and generate 100 pseudobulks per cell type.

[11]:

import time
start_time = time.time()
generate_pseudobulks(scplus_obj,
                         variable = 'ACC_Seurat_cell_type',
                         signature_key = 'Unfiltered',
                         nr_cells = 5,
                         nr_pseudobulks = 100,
                         seed=555)
time = time.time()-start_time
print(time/60)

1.0019505302111307

C. Calculate correlation

Using the pseudobulk TF expression and cistrome AUC matrix we can now assess the correlation between a TF and potential target regions.

[12]:

import time
start_time = time.time()
TF_cistrome_correlation(scplus_obj,
                        variable = 'ACC_Seurat_cell_type',
                        signature_key = 'Unfiltered',
                        out_key = 'ACC_Seurat_cell_type_unfiltered')
time = time.time()-start_time
print(time/60)

0.021760455767313638

Let’s take a look to the TFs we find here:

[13]:

pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 1000)
pd.set_option('display.colheader_justify', 'center')
pd.set_option('display.float_format', lambda x: '%.5f' % x)
display(scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_unfiltered'].sort_values('Rho', ascending=False))

	TF	Cistrome	Rho	P-value	Adjusted_p-value
150	IKZF1	IKZF1_(6418r)	0.91915	0.00000	0.00000
650	IKZF1	IKZF1_extended_(6418r)	0.91915	0.00000	0.00000
310	RUNX1	RUNX1_(507r)	0.90647	0.00000	0.00000
831	RUNX1	RUNX1_extended_(507r)	0.90647	0.00000	0.00000
585	FLI1	FLI1_extended_(12449r)	0.89567	0.00000	0.00000
104	FLI1	FLI1_(11724r)	0.89503	0.00000	0.00000
200	MEF2C	MEF2C_(14677r)	0.85657	0.00000	0.00000
706	MEF2C	MEF2C_extended_(15468r)	0.85115	0.00000	0.00000
158	IRF8	IRF8_(6862r)	0.83591	0.00000	0.00000
658	IRF8	IRF8_extended_(6862r)	0.83591	0.00000	0.00000
88	EMX2	EMX2_(5529r)	0.80001	0.00000	0.00000
569	EMX2	EMX2_extended_(6882r)	0.78655	0.00000	0.00000
858	SOX10	SOX10_extended_(9242r)	0.78392	0.00000	0.00000
331	SOX10	SOX10_(9242r)	0.78392	0.00000	0.00000
202	MEIS1	MEIS1_(30r)	0.77788	0.00000	0.00000
708	MEIS1	MEIS1_extended_(64r)	0.76204	0.00000	0.00000
11	ARNTL2	ARNTL2_(86r)	0.75733	0.00000	0.00000
486	ARNTL2	ARNTL2_extended_(86r)	0.75733	0.00000	0.00000
52	CREB5	CREB5_(92r)	0.74138	0.00000	0.00000
530	CREB5	CREB5_extended_(92r)	0.74138	0.00000	0.00000
770	PAX6	PAX6_extended_(4540r)	0.73547	0.00000	0.00000
815	REL	REL_extended_(1512r)	0.72930	0.00000	0.00000
227	NEUROD2	NEUROD2_(1236r)	0.72845	0.00000	0.00000
595	FOXN2	FOXN2_extended_(836r)	0.71751	0.00000	0.00000
820	RFX1	RFX1_extended_(7457r)	0.67600	0.00000	0.00000
302	RFX1	RFX1_(6259r)	0.67498	0.00000	0.00000
262	PAX6	PAX6_(581r)	0.65941	0.00000	0.00000
850	SMARCA4	SMARCA4_extended_(12314r)	0.64532	0.00000	0.00000
324	SMARCA4	SMARCA4_(12314r)	0.64532	0.00000	0.00000
726	MYEF2	MYEF2_extended_(3919r)	0.62438	0.00000	0.00000
560	EGR4	EGR4_extended_(6902r)	0.62326	0.00000	0.00000
36	CCDC25	CCDC25_(2025r)	0.61721	0.00000	0.00000
512	CCDC25	CCDC25_extended_(2025r)	0.61721	0.00000	0.00000
79	EGR4	EGR4_(4237r)	0.60747	0.00000	0.00000
716	MTA3	MTA3_extended_(5767r)	0.60682	0.00000	0.00000
209	MTA3	MTA3_(5767r)	0.60682	0.00000	0.00000
343	SOX8	SOX8_(7356r)	0.59894	0.00000	0.00000
871	SOX8	SOX8_extended_(8129r)	0.59815	0.00000	0.00000
10	ARNTL	ARNTL_(86r)	0.59148	0.00000	0.00000
487	ARNTL	ARNTL_extended_(86r)	0.59148	0.00000	0.00000
803	PRRX1	PRRX1_extended_(7009r)	0.58549	0.00000	0.00000
287	PRRX1	PRRX1_(6328r)	0.57687	0.00000	0.00000
739	NFE2L1	NFE2L1_extended_(7150r)	0.57328	0.00000	0.00000
184	LHX2	LHX2_(7088r)	0.57308	0.00000	0.00000
182	LARP1	LARP1_(552r)	0.57113	0.00000	0.00000
687	LARP1	LARP1_extended_(552r)	0.57113	0.00000	0.00000
478	AHR	AHR_extended_(441r)	0.56964	0.00000	0.00000
3	AHR	AHR_(441r)	0.56964	0.00000	0.00000
232	NFE2L1	NFE2L1_(6690r)	0.56898	0.00000	0.00000
96	ETS2	ETS2_(8939r)	0.56829	0.00000	0.00000
577	ETS2	ETS2_extended_(9030r)	0.56826	0.00000	0.00000
1014	ZNF821	ZNF821_extended_(446r)	0.56454	0.00000	0.00000
369	TAL1	TAL1_(5967r)	0.55784	0.00000	0.00000
503	BCL6	BCL6_extended_(455r)	0.55630	0.00000	0.00000
27	BCL6	BCL6_(455r)	0.55630	0.00000	0.00000
682	KLF6	KLF6_extended_(2680r)	0.55533	0.00000	0.00000
178	KLF6	KLF6_(2680r)	0.55533	0.00000	0.00000
826	RFX8	RFX8_extended_(4255r)	0.54445	0.00000	0.00000
689	LHX2	LHX2_extended_(9340r)	0.54426	0.00000	0.00000
447	ZNF382	ZNF382_(112r)	0.53483	0.00000	0.00000
986	ZNF382	ZNF382_extended_(112r)	0.53483	0.00000	0.00000
38	CEBPA	CEBPA_(101r)	0.53361	0.00000	0.00000
514	CEBPA	CEBPA_extended_(101r)	0.53361	0.00000	0.00000
774	PBX3	PBX3_extended_(2962r)	0.53326	0.00000	0.00000
266	PBX3	PBX3_(2962r)	0.53326	0.00000	0.00000
825	RFX7	RFX7_extended_(4837r)	0.53283	0.00000	0.00000
4	AHRR	AHRR_(348r)	0.52820	0.00000	0.00000
479	AHRR	AHRR_extended_(348r)	0.52820	0.00000	0.00000
742	NFIA	NFIA_extended_(10467r)	0.51856	0.00000	0.00000
235	NFIA	NFIA_(10467r)	0.51856	0.00000	0.00000
351	SPI1	SPI1_(12054r)	0.51377	0.00000	0.00000
457	ZNF536	ZNF536_(1607r)	0.51254	0.00000	0.00000
997	ZNF536	ZNF536_extended_(1607r)	0.51254	0.00000	0.00000
484	ARNT2	ARNT2_extended_(481r)	0.51109	0.00000	0.00000
9	ARNT2	ARNT2_(481r)	0.51109	0.00000	0.00000
219	MYEF2	MYEF2_(287r)	0.51014	0.00000	0.00000
880	SPI1	SPI1_extended_(13968r)	0.50402	0.00000	0.00000
256	OLIG2	OLIG2_(7201r)	0.50288	0.00000	0.00000
764	OLIG2	OLIG2_extended_(7201r)	0.50288	0.00000	0.00000
562	ELF1	ELF1_extended_(13965r)	0.49355	0.00000	0.00000
81	ELF1	ELF1_(13431r)	0.49258	0.00000	0.00000
308	RFXAP	RFXAP_(4793r)	0.49246	0.00000	0.00000
828	RFXAP	RFXAP_extended_(4793r)	0.49246	0.00000	0.00000
101	ETV6	ETV6_(6366r)	0.48592	0.00000	0.00000
853	SMARCC2	SMARCC2_extended_(876r)	0.48340	0.00000	0.00000
327	SMARCC2	SMARCC2_(876r)	0.48340	0.00000	0.00000
490	ATF1	ATF1_extended_(1005r)	0.48174	0.00000	0.00000
14	ATF1	ATF1_(1005r)	0.48174	0.00000	0.00000
754	NPAS2	NPAS2_extended_(86r)	0.47330	0.00000	0.00000
247	NPAS2	NPAS2_(86r)	0.47330	0.00000	0.00000
511	CBX3	CBX3_extended_(6920r)	0.46862	0.00000	0.00000
35	CBX3	CBX3_(6920r)	0.46862	0.00000	0.00000
582	ETV6	ETV6_extended_(7207r)	0.45655	0.00000	0.00000
617	HBP1	HBP1_extended_(4730r)	0.45618	0.00000	0.00000
126	HBP1	HBP1_(3641r)	0.45393	0.00000	0.00000
976	ZNF250	ZNF250_extended_(556r)	0.44956	0.00000	0.00000
437	ZNF250	ZNF250_(552r)	0.44840	0.00000	0.00000
667	KDM4B	KDM4B_extended_(8r)	0.43758	0.00000	0.00000
15	ATF2	ATF2_(3151r)	0.43729	0.00000	0.00000
491	ATF2	ATF2_extended_(3151r)	0.43729	0.00000	0.00000
46	CLOCK	CLOCK_(86r)	0.43667	0.00000	0.00000
523	CLOCK	CLOCK_extended_(86r)	0.43667	0.00000	0.00000
186	LUZP2	LUZP2_(552r)	0.43562	0.00000	0.00000
691	LUZP2	LUZP2_extended_(552r)	0.43562	0.00000	0.00000
876	SP3	SP3_extended_(9224r)	0.43501	0.00000	0.00000
714	MNT	MNT_extended_(86r)	0.43419	0.00000	0.00000
207	MNT	MNT_(86r)	0.43419	0.00000	0.00000
398	USF2	USF2_(86r)	0.43202	0.00000	0.00000
932	USF2	USF2_extended_(86r)	0.43202	0.00000	0.00000
666	KDM4A	KDM4A_extended_(8r)	0.42996	0.00000	0.00000
743	NFIB	NFIB_extended_(8840r)	0.42645	0.00000	0.00000
236	NFIB	NFIB_(8840r)	0.42645	0.00000	0.00000
838	SALL3	SALL3_extended_(236r)	0.42634	0.00000	0.00000
678	KLF2	KLF2_extended_(869r)	0.42121	0.00000	0.00000
174	KLF2	KLF2_(869r)	0.42121	0.00000	0.00000
877	SP4	SP4_extended_(8857r)	0.41944	0.00000	0.00000
347	SP3	SP3_(7086r)	0.41346	0.00000	0.00000
415	ZBTB20	ZBTB20_(142r)	0.41146	0.00000	0.00000
948	ZBTB20	ZBTB20_extended_(142r)	0.41146	0.00000	0.00000
1011	ZNF736	ZNF736_extended_(685r)	0.41029	0.00000	0.00000
471	ZNF736	ZNF736_(685r)	0.41029	0.00000	0.00000
445	ZNF341	ZNF341_(2412r)	0.40950	0.00000	0.00000
983	ZNF341	ZNF341_extended_(2412r)	0.40950	0.00000	0.00000
668	KDM4C	KDM4C_extended_(8r)	0.40902	0.00000	0.00000
348	SP4	SP4_(6696r)	0.40222	0.00000	0.00000
497	ATF7	ATF7_extended_(139r)	0.39501	0.00000	0.00000
822	RFX3	RFX3_extended_(6040r)	0.39401	0.00000	0.00000
516	CEBPD	CEBPD_extended_(475r)	0.39238	0.00000	0.00000
40	CEBPD	CEBPD_(475r)	0.39238	0.00000	0.00000
304	RFX3	RFX3_(4970r)	0.39137	0.00000	0.00000
433	ZNF148	ZNF148_(3124r)	0.38756	0.00000	0.00000
971	ZNF148	ZNF148_extended_(3124r)	0.38756	0.00000	0.00000
282	POU3F4	POU3F4_(1216r)	0.38535	0.00000	0.00000
56	CUX1	CUX1_(3292r)	0.38511	0.00000	0.00000
536	CUX1	CUX1_extended_(3292r)	0.38511	0.00000	0.00000
206	MLXIPL	MLXIPL_(86r)	0.38350	0.00000	0.00000
713	MLXIPL	MLXIPL_extended_(86r)	0.38350	0.00000	0.00000
855	SNAPC4	SNAPC4_extended_(956r)	0.38249	0.00000	0.00000
21	ATF7	ATF7_(108r)	0.37613	0.00000	0.00000
734	NEUROD2	NEUROD2_extended_(1960r)	0.37108	0.00000	0.00000
250	NR2E1	NR2E1_(3366r)	0.37015	0.00000	0.00000
396	UQCRB	UQCRB_(64r)	0.36999	0.00000	0.00000
930	UQCRB	UQCRB_extended_(64r)	0.36999	0.00000	0.00000
566	ELK3	ELK3_extended_(8266r)	0.36740	0.00000	0.00000
715	MSX1	MSX1_extended_(9402r)	0.36481	0.00000	0.00000
758	NR2E1	NR2E1_extended_(3758r)	0.36420	0.00000	0.00000
85	ELK3	ELK3_(7672r)	0.35844	0.00000	0.00000
152	ILF2	ILF2_(129r)	0.35742	0.00000	0.00000
652	ILF2	ILF2_extended_(129r)	0.35742	0.00000	0.00000
952	ZBTB37	ZBTB37_extended_(126r)	0.35490	0.00000	0.00000
418	ZBTB37	ZBTB37_(126r)	0.35490	0.00000	0.00000
208	MSX1	MSX1_(8145r)	0.35281	0.00000	0.00000
2	AGGF1	AGGF1_(2933r)	0.34778	0.00000	0.00000
477	AGGF1	AGGF1_extended_(2933r)	0.34778	0.00000	0.00000
936	VSX1	VSX1_extended_(5820r)	0.34606	0.00000	0.00000
402	VSX1	VSX1_(5820r)	0.34606	0.00000	0.00000
485	ARNT	ARNT_extended_(2151r)	0.34487	0.00000	0.00000
8	ARNT	ARNT_(2151r)	0.34487	0.00000	0.00000
175	KLF3	KLF3_(2710r)	0.34440	0.00000	0.00000
977	ZNF263	ZNF263_extended_(4118r)	0.34422	0.00000	0.00000
438	ZNF263	ZNF263_(4118r)	0.34422	0.00000	0.00000
679	KLF3	KLF3_extended_(2749r)	0.34276	0.00000	0.00000
198	MEF2A	MEF2A_(17250r)	0.33771	0.00000	0.00000
669	KDM4D	KDM4D_extended_(8r)	0.33649	0.00000	0.00000
153	ING4	ING4_(352r)	0.33589	0.00000	0.00000
653	ING4	ING4_extended_(352r)	0.33589	0.00000	0.00000
312	RXRA	RXRA_(9141r)	0.33282	0.00000	0.00000
833	RXRA	RXRA_extended_(9141r)	0.33282	0.00000	0.00000
704	MEF2A	MEF2A_extended_(17929r)	0.33153	0.00000	0.00000
372	TCF12	TCF12_(14335r)	0.32412	0.00000	0.00000
469	ZNF71	ZNF71_(311r)	0.32408	0.00000	0.00000
1009	ZNF71	ZNF71_extended_(311r)	0.32408	0.00000	0.00000
258	OTX1	OTX1_(4081r)	0.32386	0.00000	0.00000
766	OTX1	OTX1_extended_(4081r)	0.32386	0.00000	0.00000
963	ZFP64	ZFP64_extended_(2710r)	0.32344	0.00000	0.00000
999	ZNF543	ZNF543_extended_(1550r)	0.32291	0.00000	0.00000
459	ZNF543	ZNF543_(1550r)	0.32291	0.00000	0.00000
857	SOHLH2	SOHLH2_extended_(345r)	0.31969	0.00000	0.00000
285	PRDM1	PRDM1_(1931r)	0.31889	0.00000	0.00000
801	PRDM1	PRDM1_extended_(1931r)	0.31889	0.00000	0.00000
865	SOX2	SOX2_extended_(7976r)	0.31557	0.00000	0.00000
336	SOX2	SOX2_(7976r)	0.31557	0.00000	0.00000
749	NHLH2	NHLH2_extended_(2777r)	0.31482	0.00000	0.00000
242	NHLH2	NHLH2_(2777r)	0.31482	0.00000	0.00000
363	STAT6	STAT6_(2649r)	0.31476	0.00000	0.00000
892	STAT6	STAT6_extended_(2649r)	0.31476	0.00000	0.00000
559	EGR3	EGR3_extended_(11410r)	0.31457	0.00000	0.00000
1012	ZNF740	ZNF740_extended_(1970r)	0.31007	0.00000	0.00000
78	EGR3	EGR3_(7095r)	0.30646	0.00000	0.00000
169	KLF11	KLF11_(1520r)	0.30507	0.00000	0.00000
673	KLF11	KLF11_extended_(1520r)	0.30507	0.00000	0.00000
564	ELF4	ELF4_extended_(8921r)	0.30229	0.00000	0.00000
83	ELF4	ELF4_(7253r)	0.30061	0.00000	0.00000
540	DBX2	DBX2_extended_(3565r)	0.29937	0.00000	0.00000
709	MEIS2	MEIS2_extended_(1675r)	0.29895	0.00000	0.00000
901	TCF12	TCF12_extended_(15572r)	0.29634	0.00000	0.00000
944	ZBTB11	ZBTB11_extended_(394r)	0.29585	0.00000	0.00000
410	ZBTB11	ZBTB11_(394r)	0.29585	0.00000	0.00000
535	CTCF	CTCF_extended_(9207r)	0.29269	0.00000	0.00000
292	RAD21	RAD21_(5535r)	0.28986	0.00000	0.00000
808	RAD21	RAD21_extended_(5535r)	0.28986	0.00000	0.00000
928	TWIST2	TWIST2_extended_(1045r)	0.28982	0.00000	0.00000
981	ZNF282	ZNF282_extended_(82r)	0.28868	0.00000	0.00000
442	ZNF282	ZNF282_(82r)	0.28868	0.00000	0.00000
903	TCF4	TCF4_extended_(5874r)	0.28829	0.00000	0.00000
317	SETDB1	SETDB1_(3656r)	0.28808	0.00000	0.00000
841	SETDB1	SETDB1_extended_(3656r)	0.28808	0.00000	0.00000
856	SNRPB2	SNRPB2_extended_(63r)	0.28722	0.00000	0.00000
329	SNRPB2	SNRPB2_(63r)	0.28722	0.00000	0.00000
1019	ZXDB	ZXDB_extended_(124r)	0.28674	0.00000	0.00000
30	BHLHE40	BHLHE40_(86r)	0.28630	0.00000	0.00000
326	SMARCC1	SMARCC1_(12697r)	0.28565	0.00000	0.00000
852	SMARCC1	SMARCC1_extended_(12697r)	0.28565	0.00000	0.00000
45	CHURC1	CHURC1_(2084r)	0.28541	0.00000	0.00000
521	CHURC1	CHURC1_extended_(2084r)	0.28541	0.00000	0.00000
32	BRCA1	BRCA1_(120r)	0.28494	0.00000	0.00000
508	BRCA1	BRCA1_extended_(120r)	0.28494	0.00000	0.00000
731	NELFB	NELFB_extended_(3109r)	0.28350	0.00000	0.00000
224	NELFB	NELFB_(3109r)	0.28350	0.00000	0.00000
203	MEIS3	MEIS3_(684r)	0.28340	0.00000	0.00000
873	SP100	SP100_extended_(251r)	0.28215	0.00000	0.00000
851	SMARCB1	SMARCB1_extended_(5767r)	0.27961	0.00000	0.00000
325	SMARCB1	SMARCB1_(5767r)	0.27961	0.00000	0.00000
298	RCOR1	RCOR1_(12358r)	0.27788	0.00000	0.00000
814	RCOR1	RCOR1_extended_(12358r)	0.27788	0.00000	0.00000
440	ZNF274	ZNF274_(687r)	0.27413	0.00000	0.00000
979	ZNF274	ZNF274_extended_(687r)	0.27413	0.00000	0.00000
425	ZEB2	ZEB2_(877r)	0.27357	0.00000	0.00000
959	ZEB2	ZEB2_extended_(877r)	0.27357	0.00000	0.00000
699	MAZ	MAZ_extended_(11003r)	0.27271	0.00000	0.00000
194	MAZ	MAZ_(11003r)	0.27271	0.00000	0.00000
34	BRF2	BRF2_(3325r)	0.27062	0.00000	0.00000
510	BRF2	BRF2_extended_(3325r)	0.27062	0.00000	0.00000
210	MTF1	MTF1_(1826r)	0.27052	0.00000	0.00000
717	MTF1	MTF1_extended_(1826r)	0.27052	0.00000	0.00000
164	JUND	JUND_(17492r)	0.27030	0.00000	0.00000
664	JUND	JUND_extended_(18288r)	0.26845	0.00000	0.00000
374	TCF4	TCF4_(3842r)	0.26807	0.00000	0.00000
962	ZFP2	ZFP2_extended_(52r)	0.26759	0.00000	0.00000
55	CTCF	CTCF_(8149r)	0.26658	0.00000	0.00000
922	TPPP	TPPP_extended_(3109r)	0.26635	0.00000	0.00000
390	TPPP	TPPP_(3109r)	0.26635	0.00000	0.00000
794	POU3F4	POU3F4_extended_(1685r)	0.26571	0.00000	0.00000
701	MCTP2	MCTP2_extended_(2153r)	0.26339	0.00000	0.00000
195	MCTP2	MCTP2_(2153r)	0.26339	0.00000	0.00000
522	CIC	CIC_extended_(402r)	0.25952	0.00000	0.00000
760	NRF1	NRF1_extended_(7694r)	0.25530	0.00000	0.00000
452	ZNF454	ZNF454_(692r)	0.25429	0.00000	0.00000
992	ZNF454	ZNF454_extended_(692r)	0.25429	0.00000	0.00000
278	POU2F2	POU2F2_(638r)	0.25283	0.00000	0.00000
252	NRF1	NRF1_(7141r)	0.25043	0.00000	0.00000
854	SMC3	SMC3_extended_(3926r)	0.24873	0.00000	0.00000
328	SMC3	SMC3_(3926r)	0.24873	0.00000	0.00000
98	ETV3	ETV3_(3310r)	0.24196	0.00000	0.00000
26	BCL11A	BCL11A_(10556r)	0.24017	0.00000	0.00000
502	BCL11A	BCL11A_extended_(10556r)	0.24017	0.00000	0.00000
470	ZNF732	ZNF732_(59r)	0.23947	0.00000	0.00000
1010	ZNF732	ZNF732_extended_(59r)	0.23947	0.00000	0.00000
201	MEF2D	MEF2D_(5093r)	0.23931	0.00000	0.00000
707	MEF2D	MEF2D_extended_(5929r)	0.23213	0.00000	0.00000
284	POU6F2	POU6F2_(5943r)	0.23162	0.00000	0.00000
796	POU6F2	POU6F2_extended_(5961r)	0.23129	0.00000	0.00000
969	ZNF134	ZNF134_extended_(3464r)	0.22578	0.00000	0.00000
431	ZNF134	ZNF134_(3464r)	0.22578	0.00000	0.00000
47	CPSF4	CPSF4_(2476r)	0.22459	0.00000	0.00000
524	CPSF4	CPSF4_extended_(2476r)	0.22459	0.00000	0.00000
882	SRBD1	SRBD1_extended_(287r)	0.22370	0.00000	0.00000
353	SRBD1	SRBD1_(287r)	0.22370	0.00000	0.00000
444	ZNF316	ZNF316_(338r)	0.22360	0.00000	0.00000
982	ZNF316	ZNF316_extended_(338r)	0.22360	0.00000	0.00000
517	CEBPG	CEBPG_extended_(96r)	0.22088	0.00000	0.00000
41	CEBPG	CEBPG_(96r)	0.22088	0.00000	0.00000
710	MEIS3	MEIS3_extended_(720r)	0.21974	0.00000	0.00000
405	XRCC4	XRCC4_(528r)	0.21907	0.00000	0.00000
939	XRCC4	XRCC4_extended_(528r)	0.21907	0.00000	0.00000
453	ZNF467	ZNF467_(2412r)	0.21834	0.00000	0.00000
993	ZNF467	ZNF467_extended_(2412r)	0.21834	0.00000	0.00000
898	TAL1	TAL1_extended_(8409r)	0.21401	0.00000	0.00000
239	NFYA	NFYA_(2536r)	0.21293	0.00000	0.00000
579	ETV3	ETV3_extended_(4044r)	0.21273	0.00000	0.00000
817	RELB	RELB_extended_(1512r)	0.21029	0.00000	0.00000
60	DBX2	DBX2_(581r)	0.21020	0.00000	0.00000
746	NFYA	NFYA_extended_(2624r)	0.20570	0.00000	0.00000
693	MAFB	MAFB_extended_(7092r)	0.20316	0.00000	0.00000
506	BHLHE40	BHLHE40_extended_(305r)	0.20189	0.00000	0.00000
696	MAFK	MAFK_extended_(9603r)	0.20008	0.00000	0.00000
698	MAX	MAX_extended_(3847r)	0.19988	0.00000	0.00000
193	MAX	MAX_(3847r)	0.19988	0.00000	0.00000
191	MAFK	MAFK_(8903r)	0.19796	0.00000	0.00000
473	ZNF823	ZNF823_(1823r)	0.19459	0.00000	0.00000
1015	ZNF823	ZNF823_extended_(1823r)	0.19459	0.00000	0.00000
881	SPIB	SPIB_extended_(10746r)	0.19165	0.00000	0.00000
352	SPIB	SPIB_(10746r)	0.19165	0.00000	0.00000
190	MAFG	MAFG_(7092r)	0.18265	0.00000	0.00000
695	MAFG	MAFG_extended_(7092r)	0.18265	0.00000	0.00000
17	ATF4	ATF4_(227r)	0.18135	0.00000	0.00000
493	ATF4	ATF4_extended_(227r)	0.18135	0.00000	0.00000
614	GTF3A	GTF3A_extended_(124r)	0.18022	0.00000	0.00000
253	NUP107	NUP107_(532r)	0.17671	0.00000	0.00000
761	NUP107	NUP107_extended_(532r)	0.17671	0.00000	0.00000
100	ETV5	ETV5_(8138r)	0.17284	0.00000	0.00000
581	ETV5	ETV5_extended_(8138r)	0.17284	0.00000	0.00000
991	ZNF444	ZNF444_extended_(1550r)	0.17083	0.00000	0.00000
451	ZNF444	ZNF444_(1550r)	0.17083	0.00000	0.00000
275	POLR3G	POLR3G_(563r)	0.16966	0.00000	0.00000
787	POLR3G	POLR3G_extended_(563r)	0.16966	0.00000	0.00000
188	MAFB	MAFB_(6690r)	0.16820	0.00000	0.00000
288	PRRX2	PRRX2_(7204r)	0.16807	0.00000	0.00000
804	PRRX2	PRRX2_extended_(7313r)	0.16787	0.00000	0.00000
115	FOXP1	FOXP1_(1536r)	0.16681	0.00000	0.00000
7	ARID3A	ARID3A_(12437r)	0.16531	0.00000	0.00000
789	POU2F1	POU2F1_extended_(6248r)	0.16365	0.00000	0.00000
907	TEAD1	TEAD1_extended_(3109r)	0.15994	0.00000	0.00000
377	TEAD1	TEAD1_(3109r)	0.15994	0.00000	0.00000
280	POU3F2	POU3F2_(3107r)	0.15993	0.00000	0.00000
601	FOXP1	FOXP1_extended_(1691r)	0.15895	0.00000	0.00000
792	POU3F2	POU3F2_extended_(3197r)	0.15480	0.00000	0.00000
435	ZNF234	ZNF234_(85r)	0.15396	0.00000	0.00000
974	ZNF234	ZNF234_extended_(85r)	0.15396	0.00000	0.00000
515	CEBPB	CEBPB_extended_(10863r)	0.15389	0.00000	0.00000
39	CEBPB	CEBPB_(10810r)	0.15371	0.00000	0.00000
528	CREB3L2	CREB3L2_extended_(178r)	0.15238	0.00000	0.00000
155	IRF1	IRF1_(9396r)	0.15147	0.00000	0.00000
655	IRF1	IRF1_extended_(9396r)	0.15147	0.00000	0.00000
973	ZNF202	ZNF202_extended_(1970r)	0.14685	0.00000	0.00000
454	ZNF48	ZNF48_(311r)	0.14541	0.00000	0.00000
995	ZNF48	ZNF48_extended_(311r)	0.14541	0.00000	0.00000
426	ZFHX2	ZFHX2_(3510r)	0.14245	0.00000	0.00000
960	ZFHX2	ZFHX2_extended_(3510r)	0.14245	0.00000	0.00000
216	MYBL2	MYBL2_(34r)	0.14215	0.00000	0.00000
392	TRIM69	TRIM69_(25r)	0.14213	0.00000	0.00000
924	TRIM69	TRIM69_extended_(25r)	0.14213	0.00000	0.00000
824	RFX5	RFX5_extended_(9400r)	0.13924	0.00000	0.00000
370	TBL1XR1	TBL1XR1_(8264r)	0.13838	0.00000	0.00000
899	TBL1XR1	TBL1XR1_extended_(8264r)	0.13838	0.00000	0.00000
306	RFX5	RFX5_(8395r)	0.13711	0.00000	0.00000
220	MZF1	MZF1_(4946r)	0.13672	0.00000	0.00000
727	MZF1	MZF1_extended_(4946r)	0.13672	0.00000	0.00000
692	MAF	MAF_extended_(7224r)	0.13430	0.00000	0.00000
143	HMGA2	HMGA2_(576r)	0.13335	0.00000	0.00000
636	HMGA2	HMGA2_extended_(576r)	0.13335	0.00000	0.00000
43	CHD1	CHD1_(7222r)	0.13149	0.00000	0.00000
519	CHD1	CHD1_extended_(7222r)	0.13149	0.00000	0.00000
168	KLF10	KLF10_(2366r)	0.13065	0.00000	0.00000
672	KLF10	KLF10_extended_(2366r)	0.13065	0.00000	0.00000
260	PATZ1	PATZ1_(2074r)	0.12819	0.00000	0.00000
768	PATZ1	PATZ1_extended_(2074r)	0.12819	0.00000	0.00000
945	ZBTB14	ZBTB14_extended_(7936r)	0.12619	0.00000	0.00000
411	ZBTB14	ZBTB14_(7936r)	0.12619	0.00000	0.00000
187	MAF	MAF_(7092r)	0.12556	0.00000	0.00000
277	POU2F1	POU2F1_(3189r)	0.12412	0.00000	0.00000
213	MXI1	MXI1_(4449r)	0.12139	0.00000	0.00000
720	MXI1	MXI1_extended_(4449r)	0.12139	0.00000	0.00000
76	EGR1	EGR1_(11227r)	0.11974	0.00000	0.00000
376	TCF7L2	TCF7L2_(2373r)	0.11837	0.00000	0.00000
649	ID2	ID2_extended_(86r)	0.11594	0.00000	0.00001
571	EP300	EP300_extended_(20639r)	0.11543	0.00000	0.00001
90	EP300	EP300_(20639r)	0.11543	0.00000	0.00001
810	RARB	RARB_extended_(1596r)	0.11420	0.00000	0.00001
294	RARB	RARB_(1596r)	0.11420	0.00000	0.00001
482	ARID3A	ARID3A_extended_(14486r)	0.11215	0.00001	0.00001
455	ZNF485	ZNF485_(1550r)	0.11000	0.00001	0.00002
994	ZNF485	ZNF485_extended_(1550r)	0.11000	0.00001	0.00002
677	KLF16	KLF16_extended_(913r)	0.10923	0.00001	0.00002
173	KLF16	KLF16_(767r)	0.10815	0.00001	0.00002
391	TRIM28	TRIM28_(12060r)	0.10750	0.00002	0.00002
923	TRIM28	TRIM28_extended_(12060r)	0.10750	0.00002	0.00002
557	EGR1	EGR1_extended_(13127r)	0.10623	0.00002	0.00003
618	HCFC1	HCFC1_extended_(5131r)	0.10608	0.00002	0.00003
127	HCFC1	HCFC1_(5131r)	0.10608	0.00002	0.00003
906	TCF7L2	TCF7L2_extended_(2539r)	0.10275	0.00004	0.00006
222	NANOS1	NANOS1_(707r)	0.09929	0.00007	0.00010
729	NANOS1	NANOS1_extended_(707r)	0.09929	0.00007	0.00010
77	EGR2	EGR2_(11261r)	0.09730	0.00010	0.00014
558	EGR2	EGR2_extended_(13538r)	0.09297	0.00020	0.00028
646	HSF4	HSF4_extended_(225r)	0.09206	0.00023	0.00032
627	HESX1	HESX1_extended_(5137r)	0.09193	0.00023	0.00033
135	HESX1	HESX1_(5137r)	0.09193	0.00023	0.00033
1018	ZXDA	ZXDA_extended_(124r)	0.08988	0.00032	0.00045
548	DUXB	DUXB_extended_(44r)	0.08077	0.00122	0.00167
619	HDAC1	HDAC1_extended_(528r)	0.07903	0.00156	0.00212
128	HDAC1	HDAC1_(528r)	0.07903	0.00156	0.00212
532	CREBZF	CREBZF_extended_(4r)	0.07379	0.00314	0.00424
603	FOXP4	FOXP4_extended_(1691r)	0.07180	0.00406	0.00547
144	HMX1	HMX1_(4358r)	0.07067	0.00468	0.00625
640	HMX1	HMX1_extended_(4358r)	0.07067	0.00468	0.00625
609	GPANK1	GPANK1_extended_(1128r)	0.06909	0.00570	0.00757
120	GPANK1	GPANK1_(1128r)	0.06909	0.00570	0.00757
555	EBF1	EBF1_extended_(7268r)	0.06799	0.00651	0.00861
74	EBF1	EBF1_(7268r)	0.06799	0.00651	0.00861
403	WRNIP1	WRNIP1_(829r)	0.06783	0.00664	0.00876
937	WRNIP1	WRNIP1_extended_(829r)	0.06783	0.00664	0.00876
644	HSF1	HSF1_extended_(225r)	0.06315	0.01152	0.01503
972	ZNF165	ZNF165_extended_(167r)	0.06279	0.01201	0.01562
434	ZNF165	ZNF165_(167r)	0.06279	0.01201	0.01562
874	SP1	SP1_extended_(11230r)	0.06262	0.01224	0.01590
988	ZNF3	ZNF3_extended_(522r)	0.05878	0.01869	0.02410
443	ZNF3	ZNF3_(522r)	0.05878	0.01869	0.02410
890	STAT5A	STAT5A_extended_(6247r)	0.05573	0.02581	0.03294
361	STAT5A	STAT5A_(6247r)	0.05573	0.02581	0.03294
279	POU3F1	POU3F1_(4230r)	0.05547	0.02650	0.03365
791	POU3F1	POU3F1_extended_(4230r)	0.05547	0.02650	0.03365
345	SP1	SP1_(8854r)	0.05537	0.02677	0.03396
725	MYCN	MYCN_extended_(2485r)	0.05432	0.02980	0.03770
218	MYCN	MYCN_(2485r)	0.05432	0.02980	0.03770
446	ZNF354A	ZNF354A_(142r)	0.05187	0.03802	0.04786
984	ZNF354A	ZNF354A_extended_(142r)	0.05187	0.03802	0.04786
141	HIST1H2BN	HIST1H2BN_(96r)	0.05057	0.04311	0.05387
633	HIST1H2BN	HIST1H2BN_extended_(96r)	0.05057	0.04311	0.05387
110	FOXM1	FOXM1_(8310r)	0.04965	0.04708	0.05862
381	TFAP4	TFAP4_(7514r)	0.04957	0.04741	0.05888
911	TFAP4	TFAP4_extended_(7514r)	0.04957	0.04741	0.05888
529	CREB3L4	CREB3L4_extended_(202r)	0.04952	0.04765	0.05904
51	CREB3L4	CREB3L4_(202r)	0.04952	0.04765	0.05904
337	SOX21	SOX21_(4668r)	0.04947	0.04788	0.05926
461	ZNF580	ZNF580_(66r)	0.04943	0.04805	0.05932
1001	ZNF580	ZNF580_extended_(66r)	0.04943	0.04805	0.05932
594	FOXM1	FOXM1_extended_(8340r)	0.04925	0.04886	0.06017
570	ENO1	ENO1_extended_(86r)	0.04910	0.04957	0.06090
89	ENO1	ENO1_(86r)	0.04910	0.04957	0.06090
176	KLF4	KLF4_(5365r)	0.04896	0.05025	0.06144
680	KLF4	KLF4_extended_(5365r)	0.04896	0.05025	0.06144
806	PURA	PURA_extended_(8479r)	0.04877	0.05111	0.06227
290	PURA	PURA_(8479r)	0.04877	0.05111	0.06227
389	THRB	THRB_(1596r)	0.04854	0.05225	0.06350
921	THRB	THRB_extended_(1596r)	0.04854	0.05225	0.06350
875	SP2	SP2_extended_(9883r)	0.04852	0.05231	0.06351
606	GFI1B	GFI1B_extended_(28r)	0.04836	0.05310	0.06439
319	SIN3A	SIN3A_(9510r)	0.04788	0.05551	0.06707
843	SIN3A	SIN3A_extended_(9510r)	0.04788	0.05551	0.06707
62	DIABLO	DIABLO_(844r)	0.04411	0.07778	0.09289
542	DIABLO	DIABLO_extended_(844r)	0.04411	0.07778	0.09289
864	SOX21	SOX21_extended_(6425r)	0.04324	0.08382	0.09951
378	TEAD4	TEAD4_(5981r)	0.04120	0.09945	0.11739
908	TEAD4	TEAD4_extended_(5981r)	0.04120	0.09945	0.11739
829	RIOK2	RIOK2_extended_(1192r)	0.04113	0.10001	0.11778
309	RIOK2	RIOK2_(1192r)	0.04113	0.10001	0.11778
460	ZNF579	ZNF579_(2476r)	0.04038	0.10643	0.12504
1000	ZNF579	ZNF579_extended_(2476r)	0.04038	0.10643	0.12504
809	RARA	RARA_extended_(1596r)	0.04016	0.10834	0.12699
293	RARA	RARA_(1596r)	0.04016	0.10834	0.12699
334	SOX13	SOX13_(5255r)	0.03747	0.13412	0.15561
968	ZNF12	ZNF12_extended_(16r)	0.03731	0.13576	0.15716
430	ZNF12	ZNF12_(16r)	0.03731	0.13576	0.15716
861	SOX13	SOX13_extended_(6135r)	0.03502	0.16142	0.18665
80	EHF	EHF_(5266r)	0.03466	0.16579	0.19084
561	EHF	EHF_extended_(5266r)	0.03466	0.16579	0.19084
243	NKX3-1	NKX3-1_(757r)	0.03418	0.17177	0.19683
750	NKX3-1	NKX3-1_extended_(757r)	0.03418	0.17177	0.19683
639	HMGB3	HMGB3_extended_(36r)	0.03202	0.20053	0.22877
654	INSM1	INSM1_extended_(653r)	0.03149	0.20800	0.23676
154	INSM1	INSM1_(653r)	0.03149	0.20800	0.23676
688	LEF1	LEF1_extended_(1246r)	0.03134	0.21025	0.23868
866	SOX3	SOX3_extended_(6078r)	0.03129	0.21094	0.23877
338	SOX3	SOX3_(6078r)	0.03129	0.21094	0.23877
956	ZC3H11A	ZC3H11A_extended_(3904r)	0.02892	0.24766	0.27726
422	ZC3H11A	ZC3H11A_(3904r)	0.02892	0.24766	0.27726
265	PBX2	PBX2_(44r)	0.02885	0.24877	0.27789
773	PBX2	PBX2_extended_(44r)	0.02885	0.24877	0.27789
24	BARX2	BARX2_(4563r)	0.02867	0.25169	0.28055
500	BARX2	BARX2_extended_(4563r)	0.02867	0.25169	0.28055
832	RUVBL1	RUVBL1_extended_(14r)	0.02798	0.26338	0.29229
311	RUVBL1	RUVBL1_(14r)	0.02798	0.26338	0.29229
909	TEF	TEF_extended_(331r)	0.02735	0.27423	0.30368
904	TCF7	TCF7_extended_(137r)	0.02724	0.27626	0.30559
741	NFE2L3	NFE2L3_extended_(6701r)	0.02547	0.30865	0.34032
592	FOXK1	FOXK1_extended_(1529r)	0.02421	0.33308	0.36567
183	LEF1	LEF1_(1109r)	0.02386	0.34020	0.37228
837	SALL2	SALL2_extended_(236r)	0.02372	0.34296	0.37491
724	MYC	MYC_extended_(11466r)	0.02333	0.35107	0.38255
217	MYC	MYC_(11466r)	0.02333	0.35107	0.38255
914	TFE3	TFE3_extended_(86r)	0.02184	0.38272	0.41350
384	TFE3	TFE3_(86r)	0.02184	0.38272	0.41350
234	NFE2L3	NFE2L3_(5767r)	0.01918	0.44319	0.47681
848	SMAD5	SMAD5_extended_(43r)	0.01885	0.45126	0.48498
946	ZBTB17	ZBTB17_extended_(2947r)	0.01880	0.45237	0.48567
25	BATF3	BATF3_(96r)	0.01849	0.45983	0.49183
501	BATF3	BATF3_extended_(96r)	0.01849	0.45983	0.49183
400	VAX2	VAX2_(4758r)	0.01848	0.46004	0.49183
934	VAX2	VAX2_extended_(4758r)	0.01848	0.46004	0.49183
362	STAT5B	STAT5B_(225r)	0.01843	0.46142	0.49228
891	STAT5B	STAT5B_extended_(225r)	0.01843	0.46142	0.49228
597	FOXO1	FOXO1_extended_(8063r)	0.01763	0.48092	0.51255
111	FOXO1	FOXO1_(8043r)	0.01399	0.57605	0.61074
583	EXO5	EXO5_extended_(1104r)	0.01363	0.58592	0.61864
102	EXO5	EXO5_(1104r)	0.01363	0.58592	0.61864
776	PGAM2	PGAM2_extended_(957r)	0.01300	0.60337	0.63575
267	PGAM2	PGAM2_(957r)	0.01300	0.60337	0.63575
61	DDIT3	DDIT3_(59r)	0.01283	0.60808	0.63940
541	DDIT3	DDIT3_extended_(59r)	0.01283	0.60808	0.63940
978	ZNF267	ZNF267_extended_(261r)	0.01194	0.63331	0.66427
439	ZNF267	ZNF267_(261r)	0.01194	0.63331	0.66427
950	ZBTB2	ZBTB2_extended_(251r)	0.01190	0.63434	0.66427
414	ZBTB2	ZBTB2_(251r)	0.01190	0.63434	0.66427
429	ZMIZ1	ZMIZ1_(528r)	0.01141	0.64828	0.67747
967	ZMIZ1	ZMIZ1_extended_(528r)	0.01141	0.64828	0.67747
379	TEF	TEF_(231r)	0.01119	0.65462	0.68340
129	HDAC2	HDAC2_(753r)	0.01079	0.66636	0.69424
620	HDAC2	HDAC2_extended_(753r)	0.01079	0.66636	0.69424
346	SP2	SP2_(7979r)	0.01000	0.68950	0.71761
315	SAP30	SAP30_(265r)	0.00761	0.76110	0.78731
839	SAP30	SAP30_extended_(265r)	0.00761	0.76110	0.78731
468	ZNF691	ZNF691_(391r)	0.00552	0.82527	0.84429
387	THAP11	THAP11_(24r)	0.00508	0.83924	0.85601
918	THAP11	THAP11_extended_(24r)	0.00508	0.83924	0.85601
591	FOXJ3	FOXJ3_extended_(4714r)	0.00504	0.84021	0.85614
1004	ZNF664	ZNF664_extended_(25r)	0.00468	0.85170	0.86612
465	ZNF664	ZNF664_(25r)	0.00468	0.85170	0.86612
926	TRPS1	TRPS1_extended_(397r)	0.00327	0.89598	0.90484
1017	ZSCAN21	ZSCAN21_extended_(213r)	0.00315	0.89972	0.90772
231	NFATC3	NFATC3_(961r)	0.00174	0.94469	0.94655
738	NFATC3	NFATC3_extended_(961r)	0.00174	0.94469	0.94655
261	PAX5	PAX5_(4427r)	0.00052	0.98330	0.98330
769	PAX5	PAX5_extended_(4427r)	0.00052	0.98330	0.98330
136	HEY1	HEY1_(86r)	-0.00196	0.93749	0.94117
628	HEY1	HEY1_extended_(86r)	-0.00196	0.93749	0.94117
441	ZNF281	ZNF281_(4149r)	-0.00220	0.93008	0.93558
980	ZNF281	ZNF281_extended_(4149r)	-0.00220	0.93008	0.93558
66	DLX6	DLX6_(4230r)	-0.00257	0.91805	0.92530
556	EEF1AKMT3	EEF1AKMT3_extended_(1192r)	-0.00357	0.88660	0.89625
75	EEF1AKMT3	EEF1AKMT3_(1192r)	-0.00357	0.88660	0.89625
344	SOX9	SOX9_(9477r)	-0.00366	0.88377	0.89516
634	HLF	HLF_extended_(100r)	-0.00412	0.86916	0.88124
307	RFXANK	RFXANK_(4684r)	-0.00448	0.85790	0.87069
827	RFXANK	RFXANK_extended_(4684r)	-0.00448	0.85790	0.87069
1008	ZNF691	ZNF691_extended_(3411r)	-0.00524	0.83397	0.85233
18	ATF5	ATF5_(1849r)	-0.00591	0.81336	0.83294
494	ATF5	ATF5_extended_(1849r)	-0.00591	0.81336	0.83294
567	ELK4	ELK4_extended_(8636r)	-0.00623	0.80331	0.82430
86	ELK4	ELK4_(8636r)	-0.00623	0.80331	0.82430
573	ERF	ERF_extended_(7355r)	-0.00641	0.79775	0.82024
92	ERF	ERF_(7355r)	-0.00641	0.79775	0.82024
785	POLE4	POLE4_extended_(194r)	-0.00687	0.78374	0.80747
872	SOX9	SOX9_extended_(9960r)	-0.00736	0.76869	0.79276
1005	ZNF665	ZNF665_extended_(16r)	-0.00740	0.76731	0.79214
466	ZNF665	ZNF665_(16r)	-0.00740	0.76731	0.79214
23	BACH2	BACH2_(12072r)	-0.00874	0.72674	0.75330
645	HSF2	HSF2_extended_(225r)	-0.00879	0.72536	0.75263
499	BACH2	BACH2_extended_(12417r)	-0.00950	0.70412	0.73134
723	MYBL2	MYBL2_extended_(990r)	-0.00978	0.69583	0.72346
113	FOXO4	FOXO4_(1582r)	-0.01368	0.58441	0.61833
599	FOXO4	FOXO4_extended_(1604r)	-0.01372	0.58350	0.61801
204	MEOX2	MEOX2_(5105r)	-0.01410	0.57307	0.60822
722	MYBL1	MYBL1_extended_(1490r)	-0.01536	0.53929	0.57297
834	RXRB	RXRB_extended_(1596r)	-0.01639	0.51236	0.54492
313	RXRB	RXRB_(1596r)	-0.01639	0.51236	0.54492
489	ASCL1	ASCL1_extended_(6738r)	-0.02001	0.42389	0.45653
576	ETS1	ETS1_extended_(11874r)	-0.02135	0.39338	0.42412
711	MEOX2	MEOX2_extended_(6107r)	-0.02169	0.38592	0.41652
114	FOXO6	FOXO6_(1582r)	-0.02245	0.36951	0.40008
0	ACO1	ACO1_(376r)	-0.02283	0.36153	0.39185
475	ACO1	ACO1_extended_(376r)	-0.02283	0.36153	0.39185
756	NR1H2	NR1H2_extended_(6r)	-0.02307	0.35646	0.38717
248	NR1H2	NR1H2_(6r)	-0.02307	0.35646	0.38717
600	FOXO6	FOXO6_extended_(1604r)	-0.02326	0.35251	0.38370
95	ETS1	ETS1_(11737r)	-0.02370	0.34346	0.37505
894	SUPT20H	SUPT20H_extended_(679r)	-0.02388	0.33973	0.37217
365	SUPT20H	SUPT20H_(679r)	-0.02388	0.33973	0.37217
63	DLX1	DLX1_(6215r)	-0.02481	0.32140	0.35323
546	DLX6	DLX6_extended_(4647r)	-0.02482	0.32108	0.35323
13	ASCL1	ASCL1_(3842r)	-0.02520	0.31372	0.34554
762	ODC1	ODC1_extended_(3063r)	-0.02618	0.29533	0.32598
254	ODC1	ODC1_(3063r)	-0.02618	0.29533	0.32598
109	FOXJ2	FOXJ2_(2449r)	-0.02766	0.26879	0.29797
249	NR2C2	NR2C2_(1056r)	-0.02832	0.25753	0.28642
757	NR2C2	NR2C2_extended_(1056r)	-0.02832	0.25753	0.28642
721	MYB	MYB_extended_(3721r)	-0.02992	0.23159	0.25984
157	IRF3	IRF3_(4053r)	-0.03011	0.22864	0.25681
759	NR3C1	NR3C1_extended_(3088r)	-0.03025	0.22657	0.25477
251	NR3C1	NR3C1_(3088r)	-0.03025	0.22657	0.25477
238	NFIX	NFIX_(8846r)	-0.03112	0.21347	0.24056
745	NFIX	NFIX_extended_(8846r)	-0.03112	0.21347	0.24056
718	MXD3	MXD3_extended_(86r)	-0.03117	0.21270	0.24023
211	MXD3	MXD3_(86r)	-0.03117	0.21270	0.24023
728	NANOG	NANOG_extended_(9464r)	-0.03133	0.21040	0.23868
657	IRF3	IRF3_extended_(4556r)	-0.03140	0.20941	0.23809
543	DLX1	DLX1_extended_(6417r)	-0.03300	0.18707	0.21365
526	CREB3	CREB3_extended_(195r)	-0.03371	0.17777	0.20325
49	CREB3	CREB3_(195r)	-0.03371	0.17777	0.20325
12	ARX	ARX_(4213r)	-0.03432	0.17008	0.19534
580	ETV4	ETV4_extended_(10218r)	-0.03446	0.16831	0.19352
64	DLX2	DLX2_(3641r)	-0.03467	0.16577	0.19084
544	DLX2	DLX2_extended_(3641r)	-0.03467	0.16577	0.19084
160	IVD	IVD_(67r)	-0.03815	0.12714	0.14768
660	IVD	IVD_extended_(67r)	-0.03815	0.12714	0.14768
212	MXD4	MXD4_(86r)	-0.03830	0.12570	0.14634
719	MXD4	MXD4_extended_(86r)	-0.03830	0.12570	0.14634
357	STAT1	STAT1_(10312r)	-0.03846	0.12407	0.14477
886	STAT1	STAT1_extended_(10312r)	-0.03846	0.12407	0.14477
65	DLX5	DLX5_(5382r)	-0.03962	0.11315	0.13233
545	DLX5	DLX5_extended_(5382r)	-0.03962	0.11315	0.13233
99	ETV4	ETV4_(9117r)	-0.04128	0.09881	0.11690
121	GTF2B	GTF2B_(264r)	-0.04167	0.09570	0.11335
611	GTF2B	GTF2B_extended_(264r)	-0.04167	0.09570	0.11335
103	EZH2	EZH2_(1103r)	-0.04370	0.08054	0.09572
584	EZH2	EZH2_extended_(1103r)	-0.04370	0.08054	0.09572
958	ZEB1	ZEB1_extended_(497r)	-0.04397	0.07871	0.09378
424	ZEB1	ZEB1_(497r)	-0.04397	0.07871	0.09378
339	SOX4	SOX4_(7897r)	-0.04653	0.06278	0.07515
836	SALL1	SALL1_extended_(236r)	-0.04658	0.06252	0.07492
320	SIRT6	SIRT6_(1627r)	-0.04668	0.06194	0.07432
844	SIRT6	SIRT6_extended_(1627r)	-0.04668	0.06194	0.07432
867	SOX4	SOX4_extended_(9442r)	-0.04670	0.06181	0.07432
50	CREB3L1	CREB3L1_(86r)	-0.04690	0.06073	0.07312
496	ATF6B	ATF6B_extended_(178r)	-0.04754	0.05729	0.06905
20	ATF6B	ATF6B_(178r)	-0.04754	0.05729	0.06905
70	E2F4	E2F4_(499r)	-0.04804	0.05469	0.06623
303	RFX2	RFX2_(6591r)	-0.04893	0.05035	0.06150
495	ATF6	ATF6_extended_(108r)	-0.04900	0.05005	0.06135
19	ATF6	ATF6_(108r)	-0.04900	0.05005	0.06135
821	RFX2	RFX2_extended_(7423r)	-0.04926	0.04883	0.06017
605	GABPB1	GABPB1_extended_(7104r)	-0.04989	0.04600	0.05734
118	GABPB1	GABPB1_(7104r)	-0.04989	0.04600	0.05734
849	SMAD9	SMAD9_extended_(43r)	-0.05085	0.04197	0.05258
638	HMGB2	HMGB2_extended_(36r)	-0.05133	0.04009	0.05028
622	HDX	HDX_extended_(1749r)	-0.05180	0.03828	0.04808
131	HDX	HDX_(1749r)	-0.05180	0.03828	0.04808
93	ERG	ERG_(9645r)	-0.05307	0.03379	0.04264
412	ZBTB17	ZBTB17_(2476r)	-0.05313	0.03358	0.04243
949	ZBTB26	ZBTB26_extended_(86r)	-0.05569	0.02592	0.03299
416	ZBTB26	ZBTB26_(86r)	-0.05569	0.02592	0.03299
888	STAT3	STAT3_extended_(19173r)	-0.05596	0.02521	0.03225
359	STAT3	STAT3_(19173r)	-0.05596	0.02521	0.03225
931	USF1	USF1_extended_(524r)	-0.05656	0.02366	0.03035
397	USF1	USF1_(524r)	-0.05656	0.02366	0.03035
221	NANOG	NANOG_(8034r)	-0.05740	0.02167	0.02786
488	ARX	ARX_extended_(6017r)	-0.05743	0.02160	0.02781
145	HNF4G	HNF4G_(2401r)	-0.06142	0.01401	0.01810
641	HNF4G	HNF4G_extended_(2401r)	-0.06142	0.01401	0.01810
225	NELFE	NELFE_(654r)	-0.06172	0.01355	0.01755
732	NELFE	NELFE_extended_(654r)	-0.06172	0.01355	0.01755
985	ZNF35	ZNF35_extended_(23r)	-0.06316	0.01151	0.01503
264	PBX1	PBX1_(44r)	-0.06439	0.00999	0.01306
409	YY2	YY2_(3219r)	-0.06525	0.00903	0.01182
84	ELK1	ELK1_(10538r)	-0.06585	0.00841	0.01103
565	ELK1	ELK1_extended_(10538r)	-0.06585	0.00841	0.01103
943	YY2	YY2_extended_(3315r)	-0.06607	0.00820	0.01077
574	ERG	ERG_extended_(10296r)	-0.06660	0.00770	0.01013
161	JDP2	JDP2_(11500r)	-0.06714	0.00722	0.00951
778	PHF8	PHF8_extended_(829r)	-0.06861	0.00604	0.00801
269	PHF8	PHF8_(829r)	-0.06861	0.00604	0.00801
214	MYB	MYB_(2733r)	-0.07001	0.00509	0.00678
915	TFEB	TFEB_extended_(86r)	-0.07089	0.00456	0.00610
385	TFEB	TFEB_(86r)	-0.07089	0.00456	0.00610
551	E2F4	E2F4_extended_(2798r)	-0.07095	0.00452	0.00606
467	ZNF671	ZNF671_(634r)	-0.07165	0.00414	0.00556
1007	ZNF671	ZNF671_extended_(634r)	-0.07165	0.00414	0.00556
661	JDP2	JDP2_extended_(11543r)	-0.07297	0.00350	0.00472
755	NPDC1	NPDC1_extended_(92r)	-0.07556	0.00249	0.00337
138	HEYL	HEYL_(86r)	-0.07785	0.00183	0.00248
630	HEYL	HEYL_extended_(86r)	-0.07785	0.00183	0.00248
811	RARG	RARG_extended_(2912r)	-0.07934	0.00149	0.00203
295	RARG	RARG_(2912r)	-0.07934	0.00149	0.00203
483	ARID3B	ARID3B_extended_(581r)	-0.08035	0.00130	0.00177
910	TFAP2C	TFAP2C_extended_(1842r)	-0.08186	0.00105	0.00143
380	TFAP2C	TFAP2C_(1842r)	-0.08186	0.00105	0.00143
481	ARGFX	ARGFX_extended_(3626r)	-0.08309	0.00088	0.00121
6	ARGFX	ARGFX_(3626r)	-0.08309	0.00088	0.00121
373	TCF3	TCF3_(4504r)	-0.08474	0.00069	0.00095
870	SOX7	SOX7_extended_(6099r)	-0.08595	0.00058	0.00080
281	POU3F3	POU3F3_(594r)	-0.08597	0.00058	0.00080
989	ZNF415	ZNF415_extended_(20r)	-0.08655	0.00053	0.00073
449	ZNF415	ZNF415_(20r)	-0.08655	0.00053	0.00073
847	SMAD4	SMAD4_extended_(196r)	-0.08656	0.00053	0.00073
323	SMAD4	SMAD4_(196r)	-0.08656	0.00053	0.00073
159	IRF9	IRF9_(2399r)	-0.08660	0.00052	0.00073
342	SOX7	SOX7_(4918r)	-0.08667	0.00052	0.00072
1003	ZNF599	ZNF599_extended_(858r)	-0.08688	0.00050	0.00070
463	ZNF599	ZNF599_(858r)	-0.08688	0.00050	0.00070
902	TCF3	TCF3_extended_(5874r)	-0.08804	0.00042	0.00059
33	BRF1	BRF1_(563r)	-0.08883	0.00037	0.00052
509	BRF1	BRF1_extended_(563r)	-0.08883	0.00037	0.00052
406	YBX1	YBX1_(312r)	-0.08990	0.00032	0.00045
940	YBX1	YBX1_extended_(312r)	-0.08990	0.00032	0.00045
299	RELA	RELA_(9044r)	-0.09060	0.00028	0.00040
885	SRF	SRF_extended_(3748r)	-0.09224	0.00022	0.00031
797	PPARA	PPARA_extended_(36r)	-0.09350	0.00018	0.00026
659	IRF9	IRF9_extended_(3095r)	-0.09443	0.00016	0.00022
966	ZIC2	ZIC2_extended_(117r)	-0.09516	0.00014	0.00020
68	E2F1	E2F1_(2918r)	-0.09528	0.00014	0.00019
700	MBD1	MBD1_extended_(120r)	-0.09542	0.00013	0.00019
878	SP8	SP8_extended_(299r)	-0.09576	0.00013	0.00018
705	MEF2B	MEF2B_extended_(4628r)	-0.09597	0.00012	0.00017
772	PBX1	PBX1_extended_(168r)	-0.09611	0.00012	0.00017
22	BACH1	BACH1_(15444r)	-0.09683	0.00010	0.00015
199	MEF2B	MEF2B_(3647r)	-0.09736	0.00010	0.00014
432	ZNF143	ZNF143_(5277r)	-0.09812	0.00008	0.00012
970	ZNF143	ZNF143_extended_(5287r)	-0.09861	0.00008	0.00011
498	BACH1	BACH1_extended_(15532r)	-0.09902	0.00007	0.00011
747	NFYB	NFYB_extended_(2624r)	-0.09985	0.00006	0.00009
240	NFYB	NFYB_(2624r)	-0.09985	0.00006	0.00009
427	ZFHX3	ZFHX3_(16r)	-0.10068	0.00005	0.00008
961	ZFHX3	ZFHX3_extended_(16r)	-0.10068	0.00005	0.00008
686	KLRG1	KLRG1_extended_(402r)	-0.10145	0.00005	0.00007
349	SP8	SP8_(289r)	-0.10163	0.00005	0.00007
180	KLF8	KLF8_(676r)	-0.10204	0.00004	0.00006
549	E2F1	E2F1_extended_(3739r)	-0.10326	0.00004	0.00005
272	PKNOX1	PKNOX1_(44r)	-0.10373	0.00003	0.00005
816	RELA	RELA_extended_(10218r)	-0.10430	0.00003	0.00004
793	POU3F3	POU3F3_extended_(753r)	-0.10515	0.00003	0.00004
860	SOX12	SOX12_extended_(7575r)	-0.10529	0.00002	0.00004
333	SOX12	SOX12_(4438r)	-0.10647	0.00002	0.00003
683	KLF7	KLF7_extended_(771r)	-0.10742	0.00002	0.00002
897	TAF7	TAF7_extended_(2646r)	-0.11144	0.00001	0.00001
368	TAF7	TAF7_(2646r)	-0.11144	0.00001	0.00001
552	E2F5	E2F5_extended_(331r)	-0.11151	0.00001	0.00001
71	E2F5	E2F5_(331r)	-0.11151	0.00001	0.00001
428	ZFP82	ZFP82_(23r)	-0.11173	0.00001	0.00001
964	ZFP82	ZFP82_extended_(23r)	-0.11173	0.00001	0.00001
690	LHX6	LHX6_extended_(7672r)	-0.11196	0.00001	0.00001
399	VAX1	VAX1_(4785r)	-0.11321	0.00001	0.00001
933	VAX1	VAX1_extended_(4785r)	-0.11321	0.00001	0.00001
588	FOSL2	FOSL2_extended_(15421r)	-0.11381	0.00001	0.00001
916	TGIF1	TGIF1_extended_(36r)	-0.11545	0.00000	0.00001
735	NF1	NF1_extended_(5487r)	-0.11676	0.00000	0.00000
228	NF1	NF1_(5487r)	-0.11676	0.00000	0.00000
107	FOSL2	FOSL2_(14343r)	-0.11680	0.00000	0.00000
237	NFIC	NFIC_(19254r)	-0.11690	0.00000	0.00000
744	NFIC	NFIC_extended_(19254r)	-0.11690	0.00000	0.00000
142	HMGA1	HMGA1_(1300r)	-0.11735	0.00000	0.00000
635	HMGA1	HMGA1_extended_(1300r)	-0.11735	0.00000	0.00000
358	STAT2	STAT2_(2698r)	-0.11965	0.00000	0.00000
887	STAT2	STAT2_extended_(2698r)	-0.11965	0.00000	0.00000
835	RXRG	RXRG_extended_(1596r)	-0.11989	0.00000	0.00000
314	RXRG	RXRG_(1596r)	-0.11989	0.00000	0.00000
604	GABPA	GABPA_extended_(9943r)	-0.12108	0.00000	0.00000
117	GABPA	GABPA_(9677r)	-0.12263	0.00000	0.00000
527	CREB3L1	CREB3L1_extended_(178r)	-0.12335	0.00000	0.00000
356	SRF	SRF_(2591r)	-0.12348	0.00000	0.00000
421	ZBTB7B	ZBTB7B_(6347r)	-0.12418	0.00000	0.00000
955	ZBTB7B	ZBTB7B_extended_(6347r)	-0.12418	0.00000	0.00000
393	TRIP10	TRIP10_(119r)	-0.12435	0.00000	0.00000
925	TRIP10	TRIP10_extended_(119r)	-0.12435	0.00000	0.00000
58	CYB5R1	CYB5R1_(15r)	-0.12648	0.00000	0.00000
538	CYB5R1	CYB5R1_extended_(15r)	-0.12648	0.00000	0.00000
1020	ZXDC	ZXDC_extended_(124r)	-0.12774	0.00000	0.00000
181	KLF9	KLF9_(767r)	-0.12880	0.00000	0.00000
685	KLF9	KLF9_extended_(767r)	-0.12880	0.00000	0.00000
621	HDAC6	HDAC6_extended_(1000r)	-0.12940	0.00000	0.00000
130	HDAC6	HDAC6_(1000r)	-0.12940	0.00000	0.00000
647	HSF5	HSF5_extended_(126r)	-0.13016	0.00000	0.00000
148	HSF5	HSF5_(126r)	-0.13016	0.00000	0.00000
520	CHD2	CHD2_extended_(850r)	-0.13045	0.00000	0.00000
44	CHD2	CHD2_(850r)	-0.13045	0.00000	0.00000
895	SUZ12	SUZ12_extended_(654r)	-0.13070	0.00000	0.00000
366	SUZ12	SUZ12_(654r)	-0.13070	0.00000	0.00000
1002	ZNF597	ZNF597_extended_(43r)	-0.13106	0.00000	0.00000
462	ZNF597	ZNF597_(43r)	-0.13106	0.00000	0.00000
537	CXXC1	CXXC1_extended_(5r)	-0.13107	0.00000	0.00000
57	CXXC1	CXXC1_(5r)	-0.13107	0.00000	0.00000
733	NEUROD1	NEUROD1_extended_(4280r)	-0.13109	0.00000	0.00000
226	NEUROD1	NEUROD1_(4280r)	-0.13109	0.00000	0.00000
781	PKNOX1	PKNOX1_extended_(80r)	-0.13153	0.00000	0.00000
578	ETV1	ETV1_extended_(3619r)	-0.13407	0.00000	0.00000
554	E4F1	E4F1_extended_(48r)	-0.13497	0.00000	0.00000
73	E4F1	E4F1_(48r)	-0.13497	0.00000	0.00000
185	LHX6	LHX6_(4677r)	-0.13572	0.00000	0.00000
146	HOMEZ	HOMEZ_(936r)	-0.13598	0.00000	0.00000
642	HOMEZ	HOMEZ_extended_(936r)	-0.13598	0.00000	0.00000
593	FOXK2	FOXK2_extended_(1529r)	-0.13626	0.00000	0.00000
951	ZBTB33	ZBTB33_extended_(839r)	-0.13695	0.00000	0.00000
417	ZBTB33	ZBTB33_(839r)	-0.13695	0.00000	0.00000
675	KLF13	KLF13_extended_(2574r)	-0.13724	0.00000	0.00000
171	KLF13	KLF13_(2574r)	-0.13724	0.00000	0.00000
771	PAX7	PAX7_extended_(2857r)	-0.13906	0.00000	0.00000
263	PAX7	PAX7_(2712r)	-0.14049	0.00000	0.00000
97	ETV1	ETV1_(2963r)	-0.14286	0.00000	0.00000
259	PARP1	PARP1_(225r)	-0.14315	0.00000	0.00000
767	PARP1	PARP1_extended_(225r)	-0.14315	0.00000	0.00000
394	TWIST1	TWIST1_(877r)	-0.14315	0.00000	0.00000
927	TWIST1	TWIST1_extended_(877r)	-0.14315	0.00000	0.00000
996	ZNF519	ZNF519_extended_(24r)	-0.14389	0.00000	0.00000
456	ZNF519	ZNF519_(24r)	-0.14389	0.00000	0.00000
268	PGR	PGR_(78r)	-0.14639	0.00000	0.00000
777	PGR	PGR_extended_(78r)	-0.14639	0.00000	0.00000
196	MDM2	MDM2_(140r)	-0.14660	0.00000	0.00000
702	MDM2	MDM2_extended_(140r)	-0.14660	0.00000	0.00000
134	HES6	HES6_(86r)	-0.14723	0.00000	0.00000
626	HES6	HES6_extended_(86r)	-0.14723	0.00000	0.00000
301	RFC2	RFC2_(693r)	-0.15120	0.00000	0.00000
819	RFC2	RFC2_extended_(693r)	-0.15120	0.00000	0.00000
273	PLAGL1	PLAGL1_(1890r)	-0.15284	0.00000	0.00000
783	PLAGL1	PLAGL1_extended_(1890r)	-0.15284	0.00000	0.00000
332	SOX11	SOX11_(4668r)	-0.15745	0.00000	0.00000
879	SP9	SP9_extended_(299r)	-0.15757	0.00000	0.00000
805	PSMD12	PSMD12_extended_(5776r)	-0.15937	0.00000	0.00000
289	PSMD12	PSMD12_(5776r)	-0.15937	0.00000	0.00000
531	CREBL2	CREBL2_extended_(92r)	-0.16000	0.00000	0.00000
859	SOX11	SOX11_extended_(7513r)	-0.16165	0.00000	0.00000
54	CTBP2	CTBP2_(2412r)	-0.16191	0.00000	0.00000
534	CTBP2	CTBP2_extended_(2412r)	-0.16191	0.00000	0.00000
670	KDM5A	KDM5A_extended_(139r)	-0.16372	0.00000	0.00000
166	KDM5A	KDM5A_(139r)	-0.16372	0.00000	0.00000
807	R3HDM2	R3HDM2_extended_(10r)	-0.16423	0.00000	0.00000
291	R3HDM2	R3HDM2_(10r)	-0.16423	0.00000	0.00000
360	STAT4	STAT4_(225r)	-0.16553	0.00000	0.00000
889	STAT4	STAT4_extended_(225r)	-0.16553	0.00000	0.00000
681	KLF5	KLF5_extended_(2614r)	-0.16586	0.00000	0.00000
177	KLF5	KLF5_(2614r)	-0.16586	0.00000	0.00000
941	YOD1	YOD1_extended_(6112r)	-0.16680	0.00000	0.00000
632	HINFP	HINFP_extended_(3r)	-0.16733	0.00000	0.00000
140	HINFP	HINFP_(3r)	-0.16733	0.00000	0.00000
407	YOD1	YOD1_(4874r)	-0.16815	0.00000	0.00000
1016	ZRSR2	ZRSR2_extended_(59r)	-0.16855	0.00000	0.00000
474	ZRSR2	ZRSR2_(59r)	-0.16855	0.00000	0.00000
795	POU6F1	POU6F1_extended_(6482r)	-0.17243	0.00000	0.00000
350	SP9	SP9_(289r)	-0.17280	0.00000	0.00000
953	ZBTB49	ZBTB49_extended_(2712r)	-0.17283	0.00000	0.00000
419	ZBTB49	ZBTB49_(2712r)	-0.17283	0.00000	0.00000
615	GTF3C2	GTF3C2_extended_(528r)	-0.17472	0.00000	0.00000
124	GTF3C2	GTF3C2_(528r)	-0.17472	0.00000	0.00000
283	POU6F1	POU6F1_(5518r)	-0.17661	0.00000	0.00000
684	KLF8	KLF8_extended_(866r)	-0.17800	0.00000	0.00000
386	THAP1	THAP1_(28r)	-0.18028	0.00000	0.00000
919	THAP1	THAP1_extended_(28r)	-0.18028	0.00000	0.00000
752	NMI	NMI_extended_(552r)	-0.18222	0.00000	0.00000
245	NMI	NMI_(552r)	-0.18222	0.00000	0.00000
975	ZNF236	ZNF236_extended_(1306r)	-0.18309	0.00000	0.00000
436	ZNF236	ZNF236_(1306r)	-0.18309	0.00000	0.00000
863	SOX1	SOX1_extended_(5130r)	-0.18463	0.00000	0.00000
179	KLF7	KLF7_(581r)	-0.18532	0.00000	0.00000
590	FOXJ2	FOXJ2_extended_(3957r)	-0.18599	0.00000	0.00000
648	ID1	ID1_extended_(86r)	-0.18726	0.00000	0.00000
149	ID1	ID1_(86r)	-0.18726	0.00000	0.00000
631	HIF1A	HIF1A_extended_(348r)	-0.18878	0.00000	0.00000
139	HIF1A	HIF1A_(348r)	-0.18878	0.00000	0.00000
665	KAT2A	KAT2A_extended_(1405r)	-0.19147	0.00000	0.00000
165	KAT2A	KAT2A_(1405r)	-0.19147	0.00000	0.00000
59	DBP	DBP_(1004r)	-0.19249	0.00000	0.00000
539	DBP	DBP_extended_(1104r)	-0.19262	0.00000	0.00000
610	GRHL1	GRHL1_extended_(435r)	-0.19304	0.00000	0.00000
990	ZNF440	ZNF440_extended_(24r)	-0.19313	0.00000	0.00000
450	ZNF440	ZNF440_(24r)	-0.19313	0.00000	0.00000
321	SIX5	SIX5_(4470r)	-0.19468	0.00000	0.00000
845	SIX5	SIX5_extended_(4470r)	-0.19468	0.00000	0.00000
504	BCLAF1	BCLAF1_extended_(3207r)	-0.19615	0.00000	0.00000
28	BCLAF1	BCLAF1_(3207r)	-0.19615	0.00000	0.00000
413	ZBTB18	ZBTB18_(4712r)	-0.19775	0.00000	0.00000
947	ZBTB18	ZBTB18_extended_(4712r)	-0.19775	0.00000	0.00000
31	BHLHE41	BHLHE41_(86r)	-0.19778	0.00000	0.00000
507	BHLHE41	BHLHE41_extended_(86r)	-0.19778	0.00000	0.00000
553	E2F6	E2F6_extended_(495r)	-0.19807	0.00000	0.00000
72	E2F6	E2F6_(495r)	-0.19807	0.00000	0.00000
147	HOXD1	HOXD1_(4551r)	-0.19978	0.00000	0.00000
607	GMEB1	GMEB1_extended_(12r)	-0.19984	0.00000	0.00000
388	THRA	THRA_(1596r)	-0.20188	0.00000	0.00000
920	THRA	THRA_extended_(1596r)	-0.20188	0.00000	0.00000
905	TCF7L1	TCF7L1_extended_(1986r)	-0.20200	0.00000	0.00000
643	HOXD1	HOXD1_extended_(5962r)	-0.20205	0.00000	0.00000
656	IRF2	IRF2_extended_(5073r)	-0.20276	0.00000	0.00000
513	CDC5L	CDC5L_extended_(40r)	-0.20398	0.00000	0.00000
37	CDC5L	CDC5L_(40r)	-0.20398	0.00000	0.00000
784	POLE3	POLE3_extended_(124r)	-0.20405	0.00000	0.00000
5	AR	AR_(1501r)	-0.20440	0.00000	0.00000
480	AR	AR_extended_(1501r)	-0.20440	0.00000	0.00000
156	IRF2	IRF2_(4420r)	-0.20446	0.00000	0.00000
525	CREB1	CREB1_extended_(677r)	-0.20555	0.00000	0.00000
48	CREB1	CREB1_(677r)	-0.20555	0.00000	0.00000
355	SREBF2	SREBF2_(3010r)	-0.20653	0.00000	0.00000
884	SREBF2	SREBF2_extended_(3010r)	-0.20653	0.00000	0.00000
375	TCF7L1	TCF7L1_(1874r)	-0.20967	0.00000	0.00000
912	TFDP1	TFDP1_extended_(331r)	-0.21404	0.00000	0.00000
382	TFDP1	TFDP1_(331r)	-0.21404	0.00000	0.00000
505	BDP1	BDP1_extended_(585r)	-0.21497	0.00000	0.00000
29	BDP1	BDP1_(585r)	-0.21497	0.00000	0.00000
87	EMX1	EMX1_(4912r)	-0.21647	0.00000	0.00000
917	TGIF2	TGIF2_extended_(36r)	-0.21667	0.00000	0.00000
330	SOX1	SOX1_(3641r)	-0.21797	0.00000	0.00000
987	ZNF384	ZNF384_extended_(20r)	-0.21866	0.00000	0.00000
448	ZNF384	ZNF384_(20r)	-0.21866	0.00000	0.00000
748	NFYC	NFYC_extended_(317r)	-0.21953	0.00000	0.00000
257	ONECUT2	ONECUT2_(15r)	-0.22008	0.00000	0.00000
765	ONECUT2	ONECUT2_extended_(15r)	-0.22008	0.00000	0.00000
737	NFATC2	NFATC2_extended_(129r)	-0.22182	0.00000	0.00000
230	NFATC2	NFATC2_(129r)	-0.22182	0.00000	0.00000
472	ZNF76	ZNF76_(22r)	-0.22201	0.00000	0.00000
1013	ZNF76	ZNF76_extended_(22r)	-0.22201	0.00000	0.00000
241	NFYC	NFYC_(220r)	-0.22216	0.00000	0.00000
215	MYBL1	MYBL1_(77r)	-0.22234	0.00000	0.00000
125	GTPBP6	GTPBP6_(104r)	-0.22238	0.00000	0.00000
616	GTPBP6	GTPBP6_extended_(104r)	-0.22238	0.00000	0.00000
395	UBTF	UBTF_(654r)	-0.22251	0.00000	0.00000
929	UBTF	UBTF_extended_(654r)	-0.22251	0.00000	0.00000
608	GMEB2	GMEB2_extended_(4r)	-0.22305	0.00000	0.00000
119	GMEB2	GMEB2_(4r)	-0.22305	0.00000	0.00000
67	DNMT3A	DNMT3A_(25r)	-0.22457	0.00000	0.00000
547	DNMT3A	DNMT3A_extended_(25r)	-0.22457	0.00000	0.00000
942	YY1	YY1_extended_(4284r)	-0.22861	0.00000	0.00000
133	HES5	HES5_(206r)	-0.22923	0.00000	0.00000
625	HES5	HES5_extended_(206r)	-0.22923	0.00000	0.00000
900	TBP	TBP_extended_(4475r)	-0.22953	0.00000	0.00000
371	TBP	TBP_(4475r)	-0.22953	0.00000	0.00000
568	EMX1	EMX1_extended_(6815r)	-0.23113	0.00000	0.00000
112	FOXO3	FOXO3_(1582r)	-0.23117	0.00000	0.00000
598	FOXO3	FOXO3_extended_(1604r)	-0.23139	0.00000	0.00000
786	POLR2A	POLR2A_extended_(10541r)	-0.23372	0.00000	0.00000
274	POLR2A	POLR2A_(10541r)	-0.23372	0.00000	0.00000
408	YY1	YY1_(4207r)	-0.23451	0.00000	0.00000
518	CEBPZ	CEBPZ_extended_(265r)	-0.23678	0.00000	0.00000
42	CEBPZ	CEBPZ_(265r)	-0.23678	0.00000	0.00000
192	MAGEF1	MAGEF1_(523r)	-0.23794	0.00000	0.00000
697	MAGEF1	MAGEF1_extended_(523r)	-0.23794	0.00000	0.00000
297	RBBP5	RBBP5_(1087r)	-0.23949	0.00000	0.00000
813	RBBP5	RBBP5_extended_(1087r)	-0.23949	0.00000	0.00000
189	MAFF	MAFF_(7463r)	-0.24704	0.00000	0.00000
694	MAFF	MAFF_extended_(7866r)	-0.24781	0.00000	0.00000
367	TAF1	TAF1_(3172r)	-0.25330	0.00000	0.00000
896	TAF1	TAF1_extended_(3172r)	-0.25330	0.00000	0.00000
137	HEY2	HEY2_(86r)	-0.25355	0.00000	0.00000
629	HEY2	HEY2_extended_(86r)	-0.25355	0.00000	0.00000
763	OLIG1	OLIG1_extended_(2188r)	-0.25741	0.00000	0.00000
255	OLIG1	OLIG1_(2188r)	-0.25741	0.00000	0.00000
276	POU2AF1	POU2AF1_(599r)	-0.26187	0.00000	0.00000
788	POU2AF1	POU2AF1_extended_(599r)	-0.26187	0.00000	0.00000
612	GTF2F1	GTF2F1_extended_(932r)	-0.26495	0.00000	0.00000
122	GTF2F1	GTF2F1_(932r)	-0.26495	0.00000	0.00000
651	IKZF2	IKZF2_extended_(1814r)	-0.26514	0.00000	0.00000
151	IKZF2	IKZF2_(1814r)	-0.26514	0.00000	0.00000
637	HMGB1	HMGB1_extended_(36r)	-0.26710	0.00000	0.00000
840	SATB1	SATB1_extended_(301r)	-0.26854	0.00000	0.00000
316	SATB1	SATB1_(301r)	-0.26854	0.00000	0.00000
798	PPARD	PPARD_extended_(36r)	-0.27248	0.00000	0.00000
1006	ZNF66	ZNF66_extended_(66r)	-0.27415	0.00000	0.00000
464	ZNF66	ZNF66_(66r)	-0.27415	0.00000	0.00000
862	SOX15	SOX15_extended_(6945r)	-0.27637	0.00000	0.00000
335	SOX15	SOX15_(6878r)	-0.27663	0.00000	0.00000
108	FOXG1	FOXG1_(430r)	-0.27696	0.00000	0.00000
94	ESRRA	ESRRA_(528r)	-0.28076	0.00000	0.00000
575	ESRRA	ESRRA_extended_(528r)	-0.28076	0.00000	0.00000
132	HES1	HES1_(302r)	-0.28317	0.00000	0.00000
563	ELF2	ELF2_extended_(10812r)	-0.28350	0.00000	0.00000
587	FOSB	FOSB_extended_(13637r)	-0.28460	0.00000	0.00000
106	FOSB	FOSB_(13637r)	-0.28460	0.00000	0.00000
82	ELF2	ELF2_(9568r)	-0.28569	0.00000	0.00000
623	HES1	HES1_extended_(2956r)	-0.29246	0.00000	0.00000
913	TFDP2	TFDP2_extended_(331r)	-0.30290	0.00000	0.00000
383	TFDP2	TFDP2_(331r)	-0.30290	0.00000	0.00000
779	PICK1	PICK1_extended_(4544r)	-0.30300	0.00000	0.00000
270	PICK1	PICK1_(4544r)	-0.30300	0.00000	0.00000
318	SF1	SF1_(548r)	-0.30692	0.00000	0.00000
341	SOX6	SOX6_(6716r)	-0.30779	0.00000	0.00000
869	SOX6	SOX6_extended_(7658r)	-0.30812	0.00000	0.00000
846	SMAD1	SMAD1_extended_(4859r)	-0.30839	0.00000	0.00000
322	SMAD1	SMAD1_(4816r)	-0.30852	0.00000	0.00000
938	XBP1	XBP1_extended_(92r)	-0.30941	0.00000	0.00000
404	XBP1	XBP1_(92r)	-0.30941	0.00000	0.00000
116	FOXP2	FOXP2_(2027r)	-0.31269	0.00000	0.00000
602	FOXP2	FOXP2_extended_(2149r)	-0.31440	0.00000	0.00000
246	NNT	NNT_(47r)	-0.31531	0.00000	0.00000
753	NNT	NNT_extended_(47r)	-0.31531	0.00000	0.00000
775	PBX4	PBX4_extended_(268r)	-0.31767	0.00000	0.00000
663	JUNB	JUNB_extended_(16806r)	-0.31945	0.00000	0.00000
123	GTF2I	GTF2I_(396r)	-0.32089	0.00000	0.00000
613	GTF2I	GTF2I_extended_(396r)	-0.32089	0.00000	0.00000
163	JUNB	JUNB_(15981r)	-0.32092	0.00000	0.00000
364	STAU2	STAU2_(129r)	-0.32252	0.00000	0.00000
893	STAU2	STAU2_extended_(129r)	-0.32252	0.00000	0.00000
423	ZDHHC15	ZDHHC15_(85r)	-0.32462	0.00000	0.00000
957	ZDHHC15	ZDHHC15_extended_(85r)	-0.32462	0.00000	0.00000
790	POU2F2	POU2F2_extended_(4190r)	-0.32517	0.00000	0.00000
823	RFX4	RFX4_extended_(6569r)	-0.32569	0.00000	0.00000
162	JUN	JUN_(17924r)	-0.32614	0.00000	0.00000
305	RFX4	RFX4_(5602r)	-0.32803	0.00000	0.00000
662	JUN	JUN_extended_(19322r)	-0.32970	0.00000	0.00000
965	ZIC1	ZIC1_extended_(117r)	-0.33152	0.00000	0.00000
167	KDM5B	KDM5B_(126r)	-0.33200	0.00000	0.00000
671	KDM5B	KDM5B_extended_(126r)	-0.33200	0.00000	0.00000
954	ZBTB7A	ZBTB7A_extended_(775r)	-0.34002	0.00000	0.00000
420	ZBTB7A	ZBTB7A_(775r)	-0.34002	0.00000	0.00000
730	NCALD	NCALD_extended_(101r)	-0.34566	0.00000	0.00000
223	NCALD	NCALD_(101r)	-0.34566	0.00000	0.00000
624	HES4	HES4_extended_(2654r)	-0.35091	0.00000	0.00000
736	NFAT5	NFAT5_extended_(1830r)	-0.35496	0.00000	0.00000
229	NFAT5	NFAT5_(1830r)	-0.35496	0.00000	0.00000
91	EPAS1	EPAS1_(348r)	-0.35528	0.00000	0.00000
572	EPAS1	EPAS1_extended_(348r)	-0.35528	0.00000	0.00000
197	MECP2	MECP2_(685r)	-0.35559	0.00000	0.00000
703	MECP2	MECP2_extended_(685r)	-0.35559	0.00000	0.00000
883	SREBF1	SREBF1_extended_(7120r)	-0.35583	0.00000	0.00000
354	SREBF1	SREBF1_(7120r)	-0.35583	0.00000	0.00000
244	NKX6-2	NKX6-2_(6335r)	-0.35669	0.00000	0.00000
751	NKX6-2	NKX6-2_extended_(7201r)	-0.35833	0.00000	0.00000
172	KLF15	KLF15_(3441r)	-0.35998	0.00000	0.00000
676	KLF15	KLF15_extended_(3468r)	-0.36034	0.00000	0.00000
712	MITF	MITF_extended_(86r)	-0.36169	0.00000	0.00000
205	MITF	MITF_(86r)	-0.36169	0.00000	0.00000
492	ATF3	ATF3_extended_(14221r)	-0.37251	0.00000	0.00000
818	REST	REST_extended_(5605r)	-0.37460	0.00000	0.00000
533	CREM	CREM_extended_(171r)	-0.37861	0.00000	0.00000
53	CREM	CREM_(171r)	-0.37861	0.00000	0.00000
16	ATF3	ATF3_(12847r)	-0.37888	0.00000	0.00000
596	FOXN3	FOXN3_extended_(836r)	-0.38157	0.00000	0.00000
780	PKM	PKM_extended_(685r)	-0.39253	0.00000	0.00000
271	PKM	PKM_(685r)	-0.39253	0.00000	0.00000
300	REST	REST_(2933r)	-0.39328	0.00000	0.00000
842	SF1	SF1_extended_(763r)	-0.39691	0.00000	0.00000
800	PRDM11	PRDM11_extended_(826r)	-0.40485	0.00000	0.00000
935	VEZF1	VEZF1_extended_(2975r)	-0.41023	0.00000	0.00000
401	VEZF1	VEZF1_(2975r)	-0.41023	0.00000	0.00000
998	ZNF540	ZNF540_extended_(214r)	-0.42891	0.00000	0.00000
458	ZNF540	ZNF540_(214r)	-0.42891	0.00000	0.00000
286	PRNP	PRNP_(834r)	-0.43229	0.00000	0.00000
802	PRNP	PRNP_extended_(834r)	-0.43229	0.00000	0.00000
586	FOS	FOS_extended_(17541r)	-0.44315	0.00000	0.00000
105	FOS	FOS_(17541r)	-0.44315	0.00000	0.00000
799	PPARG	PPARG_extended_(36r)	-0.44770	0.00000	0.00000
476	ADARB1	ADARB1_extended_(493r)	-0.47795	0.00000	0.00000
1	ADARB1	ADARB1_(493r)	-0.47795	0.00000	0.00000
170	KLF12	KLF12_(2792r)	-0.47878	0.00000	0.00000
782	PKNOX2	PKNOX2_extended_(36r)	-0.48793	0.00000	0.00000
296	RB1	RB1_(817r)	-0.49248	0.00000	0.00000
812	RB1	RB1_extended_(817r)	-0.49248	0.00000	0.00000
674	KLF12	KLF12_extended_(2843r)	-0.49319	0.00000	0.00000
233	NFE2L2	NFE2L2_(8999r)	-0.49746	0.00000	0.00000
589	FOXG1	FOXG1_extended_(1956r)	-0.49862	0.00000	0.00000
740	NFE2L2	NFE2L2_extended_(9438r)	-0.50545	0.00000	0.00000
550	E2F3	E2F3_extended_(486r)	-0.53414	0.00000	0.00000
69	E2F3	E2F3_(397r)	-0.53585	0.00000	0.00000
868	SOX5	SOX5_extended_(7972r)	-0.55393	0.00000	0.00000
340	SOX5	SOX5_(6811r)	-0.56024	0.00000	0.00000
830	RREB1	RREB1_extended_(446r)	-0.57808	0.00000	0.00000

We can filter cistromes based on their correlation values (or p-value). We recommend however to use these values as a method to prioritize eGRNs, without applying a strict threshold.

[14]:

print('Number of unfiltered cistromes:')
print(scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_unfiltered'].shape[0])
print('Number of cistromes with padj < 0.0001:')
print(scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_unfiltered'][scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_unfiltered']['Adjusted_p-value'] < 0.0001].shape[0])

Number of unfiltered cistromes:
1021
Number of cistromes with padj < 0.0001:
703

[15]:

# Save
import pickle
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  pickle.dump(scplus_obj, f)

D. Visualization

I. TF expression versus AUC values

We can visualize TF versus cistrome expression:

[16]:

%matplotlib inline
sns.set_style("white")
colors = ["#E9842C","#F8766D", "#BC9D00", "#00C0B4", "#9CA700", "#6FB000", "#00B813", "#00BD61", "#00C08E", "#00BDD4",
           "#00A7FF", "#7F96FF", "#E26EF7", "#FF62BF", "#D69100", "#BC81FF"]
categories = sorted(set(scplus_obj.metadata_cell['ACC_Seurat_cell_type']))
color_dict = dict(zip(categories, colors[0:len(categories)]))
prune_plot(scplus_obj,
           'IKZF1',
           pseudobulk_variable = 'ACC_Seurat_cell_type',
           show_dot_plot = True,
           show_line_plot = False,
           color_dict = color_dict,
           use_pseudobulk = True,
           signature_key = 'Unfiltered',
           seed=555)

II. Dot plot

We can generate a dotplot as well based on the AUC values or the motif enrichment results.

[17]:

# Load functions
from scenicplus.plotting.dotplot import *
# Load data
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()

In this example we will select a subset of the cistromes based on the TF-AUC correlation.

[18]:

# Direct cistromes
df_corr = scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_unfiltered']
selected_cistromes_direct = df_corr[(~df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] > 0.4)]
# Extended cistromes
df_corr = scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_unfiltered']
selected_cistromes_extended = df_corr[(df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] > 0.4)]
# Only use extended if direct not available
selected_cistromes_extended = selected_cistromes_extended[~selected_cistromes_extended['TF'].isin(selected_cistromes_direct['TF'])]
# Combine
selected_cistromes = pd.concat([selected_cistromes_direct, selected_cistromes_extended])
len(selected_cistromes)

[18]:

We first generate the dotplot dataframe, that contains information about the cistrome enrichment (AUC in this case) and TF expression per group.

[20]:

dotplot_df = generate_dotplot_df_AUC(scplus_obj,
                       'Cistromes_AUC',
                       'Unfiltered',
                       'ACC_Seurat_cell_type',
                       subset = None,
                       subset_cistromes = selected_cistromes['Cistrome'].tolist(),
                       use_pseudobulk = False,
                       normalize_expression = False,
                       standardize_expression = True,
                       standardize_auc = True)

[23]:

import os
import plotly.io as pio
# Kaleido options to save plot
pio.kaleido.scope.chromium_args = tuple([arg for arg in pio.kaleido.scope.chromium_args if arg != "--disable-dev-shm-usage"])
dotplot(dotplot_df,
         ax = None,
         order_cistromes_by_max = 'TF_expression',
         cluster = 'group', #can be group, TF or both
         color_var = 'Cistromes_AUC',
         size_var = 'TF_expression',
         order_group = None,
         order_cistromes = None,
         n_clust = 10,
         min_point_size = 1,
         max_point_size = 15,
         cmap = 'viridis',
         vmin = 0,
         vmax = 1,
         x_tick_rotation = 45,
         x_tick_ha = 'right',
         fontsize  = 9,
         z_score_expr = False,
         z_score_enr = False,
         grid_color = 'grey',
         grid_lw = 0.5,
         highlight = None,
         highlight_lw = 1,
         use_plotly = True,
        save='/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/dotplot_AUC.pdf')

We can also make these dotplot based on the cisTarget and DEM motif enrichment results.

[25]:

# CTX df
dotplot_df = generate_dotplot_df_motif_enrichment(scplus_obj,
                       'CTX_DARs_All',
                       'ACC_Seurat_cell_type',
                       subset = None,
                       subset_TFs = list(set(selected_cistromes.TF)),
                       use_pseudobulk = False,
                       normalize_expression = True,
                       standardize_expression = False)
# Plot
dotplot(dotplot_df,
                     ax = None,
                     region_set_key = 'TF',
                     order_cistromes_by_max = 'TF_expression',
                     cluster = 'group', #can be group, TF or both
                     color_var = 'NES',
                     size_var = 'TF_expression',
                     order_group = None,
                     order_cistromes = None,
                     n_clust = 10,
                     min_point_size = 1,
                     max_point_size = 15,
                     cmap = 'viridis',
                     vmin = 0,
                     vmax = 1,
                     x_tick_rotation = 45,
                     x_tick_ha = 'right',
                     fontsize  = 9,
                     z_score_expr = False,
                     z_score_enr = False,
                     grid_color = 'grey',
                     grid_lw = 0.5,
                     highlight = None,
                     highlight_lw = 1,
                     use_plotly = True)

[26]:

# DEM df
dotplot_df = generate_dotplot_df_motif_enrichment(scplus_obj,
                       'DEM_DARs_All',
                       'ACC_Seurat_cell_type',
                       subset = None,
                       subset_TFs = list(set(selected_cistromes.TF)),
                       use_pseudobulk = False,
                       normalize_expression = True,
                       standardize_expression = False)
# Plot
dotplot(dotplot_df,
                     ax = None,
                     region_set_key = 'TF',
                     order_cistromes_by_max = 'TF_expression',
                     cluster = 'group', #can be group, TF or both
                     color_var = 'Log2FC',
                     size_var = 'TF_expression',
                     order_group = None,
                     order_cistromes = None,
                     n_clust = 10,
                     min_point_size = 1,
                     max_point_size = 15,
                     cmap = 'viridis',
                     vmin = 0,
                     vmax = 1,
                     x_tick_rotation = 45,
                     x_tick_ha = 'right',
                     fontsize  = 9,
                     z_score_expr = False,
                     z_score_enr = False,
                     grid_color = 'grey',
                     grid_lw = 0.5,
                     highlight = None,
                     highlight_lw = 1,
                     use_plotly = True)

In addition, it is also possible to do it for motif enrichment tables that are not linked to a metadata variable, such as topics. In this cases, we will have to provide a dictionary with the barcodes to use to calculate the TF expresssion in each group.

[27]:

# Binarized cell-topic distributions from pycisTopic
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/atac/pycistopic/topic_binarization/binarized_cell_topic.pkl', 'rb')
binarized_cell_topic = pickle.load(infile)
infile.close()
# Create barcode dictionary
barcode_groups = {x: binarized_cell_topic[x].index.tolist() for x in binarized_cell_topic.keys()}

[28]:

# Generate dotplot
dotplot_df = generate_dotplot_df_motif_enrichment(scplus_obj,
                       'CTX_Topics_All',
                       group_variable = None,
                       barcode_groups = barcode_groups,
                       subset = None,
                       subset_TFs = list(set(selected_cistromes.TF)),
                       use_pseudobulk = False,
                       normalize_expression = True,
                       standardize_expression = True)

dotplot(dotplot_df,
                     ax = None,
                     region_set_key = 'TF',
                     order_cistromes_by_max = 'TF_expression',
                     cluster = 'group', #can be group, TF or both
                     color_var = 'NES',
                     size_var = 'TF_expression',
                     order_group = None,
                     order_cistromes = None,
                     n_clust = 10,
                     min_point_size = 1,
                     max_point_size = 15,
                     cmap = 'viridis',
                     vmin = 0,
                     vmax = 1,
                     x_tick_rotation = 45,
                     x_tick_ha = 'right',
                     fontsize  = 9,
                     z_score_expr = False,
                     z_score_enr = False,
                     grid_color = 'grey',
                     grid_lw = 0.5,
                     highlight = None,
                     highlight_lw = 1,
                     use_plotly = True)

4. Infer enhancer to gene relationships

To infer enhancer-to-gene relationships, we exploit correlation between region accessibility and gene expression. In addition, to assess no non-linear relationships, we also use Gradient Boosting Machines. More details on these steps can be found in the tutorial r2g_advanced.ipynb.

A. Get search space

We first need to define the search space around the gene. Here we will use 150kb upstream/downtream the gene, but TAD boundaries can be also used. WARNING: Make sure that the specified biomart_host matches your genome assembly.

[29]:

import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()

[30]:

from scenicplus.enhancer_to_gene import get_search_space, calculate_regions_to_genes_relationships, GBM_KWARGS
get_search_space(scplus_obj,
                 biomart_host = 'http://www.ensembl.org',
                 species = 'hsapiens',
                 assembly = 'hg38',
                 upstream = [1000, 150000],
                 downstream = [1000, 150000])

2022-01-04 17:50:07,795 R2G          INFO     Downloading gene annotation from biomart dataset: hsapiens_gene_ensembl

/opt/venv/lib/python3.8/site-packages/scenicplus/enhancer_to_gene.py:197: DtypeWarning:

Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.

2022-01-04 17:50:40,505 R2G          INFO     Downloading chromosome sizes from: http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes
2022-01-04 17:50:42,099 R2G          INFO     Extending promoter annotation to 10 bp upstream and 10 downstream
2022-01-04 17:50:44,632 R2G          INFO     Extending search space to:
                                                        150000 bp downstream of the end of the gene.
                                                        150000 bp upstream of the start of the gene.
2022-01-04 17:51:07,232 R2G          INFO     Intersecting with regions.

join: Strand data from other will be added as strand data to self.
If this is undesired use the flag apply_strand_suffix=False.
To turn off the warning set apply_strand_suffix to True or False.

2022-01-04 17:51:08,592 R2G          INFO     Calculating distances from region to gene
2022-01-04 17:55:43,084 R2G          INFO     Imploding multiple entries per region and gene
2022-01-04 17:58:01,862 R2G          INFO     Done!

B. Enhancer-to-gene models

Enhancer-to-gene models can be done using correlation, random forest (RF) or Gradient Boosting Machines (GBM). GBMs are a much faster alternative to RFs.

[31]:

calculate_regions_to_genes_relationships(scplus_obj,
                    ray_n_cpu = 20,
                    _temp_dir = '/scratch/leuven/313/vsc31305/ray_spill',
                    importance_scoring_method = 'GBM',
                    importance_scoring_kwargs = GBM_KWARGS)

2022-01-04 17:59:17,845 R2G          INFO     Calculating region to gene importances, using GBM method

2022-01-04 17:59:30,930 INFO services.py:1338 -- View the Ray dashboard at http://127.0.0.1:8267
initializing:  22%|██▏       | 3225/14548 [04:27<15:17, 12.33it/s]

(score_regions_to_single_gene_ray pid=34847)

initializing:  27%|██▋       | 3905/14548 [05:23<14:24, 12.30it/s]

(score_regions_to_single_gene_ray pid=34848)

initializing:  27%|██▋       | 3953/14548 [05:27<14:34, 12.11it/s]

(score_regions_to_single_gene_ray pid=34840)

initializing:  54%|█████▍    | 7836/14548 [11:03<09:18, 12.01it/s]  (score_regions_to_single_gene_ray pid=34844)
initializing:  68%|██████▊   | 9874/14548 [13:49<06:23, 12.20it/s]

(score_regions_to_single_gene_ray pid=34855)

initializing: 100%|██████████| 14548/14548 [20:26<00:00, 11.86it/s]
Running using 20 cores: 100%|██████████| 14548/14548 [00:35<00:00, 408.74it/s]

2022-01-04 18:20:38,987 R2G          INFO     Took 1281.1407558918 seconds
2022-01-04 18:20:38,990 R2G          INFO     Calculating region to gene correlation, using SR method

2022-01-04 18:20:53,862 INFO services.py:1338 -- View the Ray dashboard at http://127.0.0.1:8267
initializing:  68%|██████▊   | 9871/14548 [13:15<06:17, 12.39it/s]  (score_regions_to_single_gene_ray pid=20985)
initializing:  84%|████████▍ | 12185/14548 [16:17<03:03, 12.88it/s](score_regions_to_single_gene_ray pid=20982)
initializing:  98%|█████████▊| 14275/14548 [19:01<00:21, 12.94it/s](score_regions_to_single_gene_ray pid=20988)
initializing: 100%|██████████| 14548/14548 [19:23<00:00, 12.51it/s]
Running using 20 cores: 100%|██████████| 14548/14548 [00:50<00:00, 288.45it/s]

2022-01-04 18:41:13,309 R2G          INFO     Took 1234.3179178237915 seconds
2022-01-04 18:41:26,108 R2G          INFO     Done!

[32]:

# Save
import pickle
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  pickle.dump(scplus_obj, f)

5. Infer TF to gene relationships

The next step is to infer relationships between TFs and target genes based on expression. We will use similar approaches as for the enhancer-to-gene relationships (GBM/RF and correlation).

[33]:

import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()

[ ]:

from scenicplus.TF_to_gene import *
tf_file = '/staging/leuven/stg_00002/lcb/cflerin/resources/allTFs_hg38.txt'
calculate_TFs_to_genes_relationships(scplus_obj,
                    tf_file = tf_file,
                    ray_n_cpu = 20,
                    method = 'GBM',
                    _temp_dir = '/scratch/leuven/313/vsc31305/ray_spill',
                    key= 'TF2G_adj')

[ ]:

# Save
import pickle
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  pickle.dump(scplus_obj, f)

If you have run SCENIC before in the gene expression matrix, it is possible to directly load adjancencies from that pipeline:

[ ]:

load_TF2G_adj_from_file(scplus_obj,
                        f_adj = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/rna/vsn/single_sample_scenic_HQ/out/scenic/10x_multiome_brain_HQ/arboreto_with_multiprocessing/10x_multiome_brain_HQ__adj.tsv',
                        inplace = True,
                        key= 'TF2G_adj')

6. Build eGRNs

The last step is to build the eGRNs using a recovery approach (GSEA). The ranking to use will be based on the TF-2-gene importances, while gene sets will be derived with different thresholding methods on the enhancer-to-gene relationships and the unfiltered cistromes.

[1]:

import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()

[ ]:

# Load functions
from scenicplus.grn_builder.gsea_approach import build_grn

build_grn(scplus_obj,
         min_target_genes = 10,
         adj_pval_thr = 1,
         min_regions_per_gene = 0,
         quantiles = (0.85, 0.90, 0.95),
         top_n_regionTogenes_per_gene = (5, 10, 15),
         top_n_regionTogenes_per_region = (),
         binarize_using_basc = True,
         rho_dichotomize_tf2g = True,
         rho_dichotomize_r2g = True,
         rho_dichotomize_eregulon = True,
         rho_threshold = 0.05,
         keep_extended_motif_annot = True,
         merge_eRegulons = True,
         order_regions_to_genes_by = 'importance',
         order_TFs_to_genes_by = 'importance',
         key_added = 'eRegulons_importance',
         cistromes_key = 'Unfiltered',
         disable_tqdm = False, #If running in notebook, set to True
         ray_n_cpu = 20,
         _temp_dir = '/scratch/leuven/313/vsc31305/ray_spill')

To access the eGRNs:

[ ]:

import dill
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  dill.dump(scplus_obj, f)

7. Exploring SCENIC+ results

A. Generate eRegulon metadata

As a first step, we will format the eGRN metadata. This will integrate the results from the inference of enhancer-to-gene and TF-to-gene relationships and the eRegulon construction in one pandas dataframe that can be used for further exploration.

[2]:

import dill
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = dill.load(infile)
infile.close()

[3]:

from scenicplus.utils import format_egrns
format_egrns(scplus_obj, eregulons_key = 'eRegulons_importance', TF2G_key = 'TF2G_adj', key_added = 'eRegulon_metadata')

The eRegulon metadata will look as:

[4]:

scplus_obj.uns['eRegulon_metadata'][0:10]

[4]:

	Region_signature_name	Gene_signature_name	TF	is_extended	Region	Gene	R2G_importance	R2G_rho	R2G_importance_x_rho	R2G_importance_x_abs_rho	TF2G_importance	TF2G_regulation	TF2G_rho	TF2G_importance_x_abs_rho	TF2G_importance_x_rho
0	ACO1_+_+_(24r)	ACO1_+_+_(23g)	ACO1	False	chr1:244918651-244919151	HNRNPU	0.021839	0.085878	0.001876	0.001876	0.236510	1	0.122128	0.028884	0.028884
1	ACO1_+_+_(24r)	ACO1_+_+_(23g)	ACO1	False	chr10:88991114-88991614	FAS	0.023378	0.215477	0.005037	0.005037	0.457794	1	0.162854	0.074554	0.074554
2	ACO1_+_+_(24r)	ACO1_+_+_(23g)	ACO1	False	chr10:88990599-88991099	FAS	0.031784	0.212687	0.006760	0.006760	0.457794	1	0.162854	0.074554	0.074554
3	ACO1_+_+_(24r)	ACO1_+_+_(23g)	ACO1	False	chrX:107676906-107677406	MID2	0.021056	0.098110	0.002066	0.002066	0.663257	1	0.165609	0.109842	0.109842
4	ACO1_+_+_(24r)	ACO1_+_+_(23g)	ACO1	False	chr16:1556214-1556714	IFT140	0.015752	0.098841	0.001557	0.001557	0.336581	1	0.095788	0.032240	0.032240
5	ACO1_+_+_(24r)	ACO1_+_+_(23g)	ACO1	False	chr10:103692953-103693453	SH3PXD2A	0.004005	0.146456	0.000587	0.000587	4.731638	1	0.200345	0.947960	0.947960
6	ACO1_+_+_(24r)	ACO1_+_+_(23g)	ACO1	False	chr19:57527137-57527637	ZNF549	0.078793	0.083988	0.006618	0.006618	0.522762	1	0.154494	0.080764	0.080764
7	ACO1_+_+_(24r)	ACO1_+_+_(23g)	ACO1	False	chr22:36323698-36324198	TXN2	0.020686	0.073909	0.001529	0.001529	1.298719	1	0.160698	0.208702	0.208702
8	ACO1_+_+_(24r)	ACO1_+_+_(23g)	ACO1	False	chr6:15365250-15365750	JARID2	0.025076	0.174829	0.004384	0.004384	0.678238	1	0.231925	0.157300	0.157300
9	ACO1_+_+_(24r)	ACO1_+_+_(23g)	ACO1	False	chr10:126525383-126525883	ADAM12	0.027865	0.067947	0.001893	0.001893	0.233867	1	0.090295	0.021117	0.021117

The first sign in the eRegulon name indicates the relationship between TF and gene; while the second indicates the relationship between region and gene. Additional columns can be added by the user, for example the enrichment of the TF motif in the regions.

B. Assesing eGRN enrichment in cells

Next, we can score the eRegulons in the cells to assess how enriched target regions and target genes are enriched in each cell. We will score the region-based regulons on the scATAC-seq layer, while gene-based eRegulons will be scored using the transcriptome layer.

[40]:

# Format eRegulons
from scenicplus.eregulon_enrichment import *
get_eRegulons_as_signatures(scplus_obj, eRegulon_metadata_key='eRegulon_metadata', key_added='eRegulon_signatures')

[6]:

## Score chromatin layer
# Region based raking
from scenicplus.cistromes import *
import time
start_time = time.time()
region_ranking = make_rankings(scplus_obj, target='region')
# Score region regulons
score_eRegulons(scplus_obj,
                ranking = region_ranking,
                eRegulon_signatures_key = 'eRegulon_signatures',
                key_added = 'eRegulon_AUC',
                enrichment_type= 'region',
                auc_threshold = 0.05,
                normalize = False,
                n_cpu = 1)
time = time.time()-start_time
print(time/60)

100%|██████████| 832/832 [00:26<00:00, 31.16it/s]

2.8363844593365988

[7]:

## Score transcriptome layer
# Gene based raking
from scenicplus.cistromes import *
import time
start_time = time.time()
gene_ranking = make_rankings(scplus_obj, target='gene')
# Score gene regulons
score_eRegulons(scplus_obj,
                gene_ranking,
                eRegulon_signatures_key = 'eRegulon_signatures',
                key_added = 'eRegulon_AUC',
                enrichment_type = 'gene',
                auc_threshold = 0.05,
                normalize= False,
                n_cpu = 1)
time = time.time()-start_time
print(time/60)

100%|██████████| 832/832 [00:07<00:00, 104.80it/s]

0.21863729159037273

Next we can assess the relationship between the TF expression and the eRegulons; in other words, whether genes expressed/repressed and regions accessible/closed when the TF is present. Due to the amount of drop-outs, and the variability in cell types proportions, using directly the AUC cistrome matrix can result in noisy correlations. Here, we use pseudobulks, in which we sample a number of cells per cell type. In this example, we merge 5 cells per pseudobulk and generate 100 pseudobulks per cell type.

[8]:

# Generate pseudobulks
import time
start_time = time.time()
generate_pseudobulks(scplus_obj,
                         variable = 'ACC_Seurat_cell_type',
                         auc_key = 'eRegulon_AUC',
                         signature_key = 'Gene_based',
                         nr_cells = 5,
                         nr_pseudobulks = 100,
                         seed=555)
generate_pseudobulks(scplus_obj,
                         variable = 'ACC_Seurat_cell_type',
                         auc_key = 'eRegulon_AUC',
                         signature_key = 'Region_based',
                         nr_cells = 5,
                         nr_pseudobulks = 100,
                         seed=555)
time = time.time()-start_time
print(time/60)

1.9503313183784485

[9]:

# Correlation between TF and eRegulons
import time
start_time = time.time()
TF_cistrome_correlation(scplus_obj,
                        variable = 'ACC_Seurat_cell_type',
                        auc_key = 'eRegulon_AUC',
                        signature_key = 'Gene_based',
                        out_key = 'ACC_Seurat_cell_type_eGRN_gene_based')
TF_cistrome_correlation(scplus_obj,
                        variable = 'ACC_Seurat_cell_type',
                        auc_key = 'eRegulon_AUC',
                        signature_key = 'Region_based',
                        out_key = 'ACC_Seurat_cell_type_eGRN_region_based')
time = time.time()-start_time
print(time/60)

0.032043548425038655

We observe that positively correlated gene-based regulons show a positive TF-gene correlation as well (first sign in the name), while negatively correlated ones show a negative TF-gene correlation.

[10]:

pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 1000)
pd.set_option('display.colheader_justify', 'center')
pd.set_option('display.float_format', lambda x: '%.5f' % x)
display(scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_eGRN_gene_based'].sort_values('Rho', ascending=False))

	TF	Cistrome	Rho	P-value	Adjusted_p-value
557	RFX4	RFX4_+_-_(37g)	0.92784	0.00000	0.00000
253	IKZF1	IKZF1_+_+_(265g)	0.92577	0.00000	0.00000
255	IKZF1	IKZF1_extended_+_+_(265g)	0.92577	0.00000	0.00000
560	RFX4	RFX4_extended_+_-_(43g)	0.92386	0.00000	0.00000
172	FLI1	FLI1_+_+_(274g)	0.89842	0.00000	0.00000
174	FLI1	FLI1_extended_+_+_(290g)	0.89731	0.00000	0.00000
41	BCL11A	BCL11A_+_+_(874g)	0.89626	0.00000	0.00000
42	BCL11A	BCL11A_extended_+_+_(874g)	0.89626	0.00000	0.00000
269	IRF8	IRF8_+_+_(165g)	0.89555	0.00000	0.00000
270	IRF8	IRF8_extended_+_+_(165g)	0.89555	0.00000	0.00000
575	RUNX1	RUNX1_extended_+_+_(54g)	0.89246	0.00000	0.00000
574	RUNX1	RUNX1_+_+_(54g)	0.89246	0.00000	0.00000
638	SOX5	SOX5_+_-_(656g)	0.88983	0.00000	0.00000
641	SOX5	SOX5_extended_+_-_(705g)	0.88853	0.00000	0.00000
754	THRB	THRB_+_+_(198g)	0.88745	0.00000	0.00000
756	THRB	THRB_extended_+_+_(198g)	0.88745	0.00000	0.00000
459	OLIG2	OLIG2_+_+_(102g)	0.88460	0.00000	0.00000
461	OLIG2	OLIG2_extended_+_+_(95g)	0.88448	0.00000	0.00000
145	EMX2	EMX2_extended_+_+_(260g)	0.88445	0.00000	0.00000
144	EMX2	EMX2_+_+_(260g)	0.88346	0.00000	0.00000
363	MEF2C	MEF2C_extended_+_-_(203g)	0.87798	0.00000	0.00000
106	EBF1	EBF1_+_-_(16g)	0.86981	0.00000	0.00000
107	EBF1	EBF1_extended_+_-_(16g)	0.86981	0.00000	0.00000
360	MEF2C	MEF2C_+_-_(159g)	0.86918	0.00000	0.00000
328	LEF1	LEF1_extended_+_+_(17g)	0.86885	0.00000	0.00000
359	MEF2C	MEF2C_+_+_(1761g)	0.86788	0.00000	0.00000
362	MEF2C	MEF2C_extended_+_+_(1788g)	0.86694	0.00000	0.00000
458	OLIG1	OLIG1_extended_+_-_(16g)	0.86512	0.00000	0.00000
457	OLIG1	OLIG1_+_-_(16g)	0.86512	0.00000	0.00000
825	ZNF536	ZNF536_+_+_(143g)	0.86114	0.00000	0.00000
827	ZNF536	ZNF536_extended_+_+_(143g)	0.86114	0.00000	0.00000
327	LEF1	LEF1_+_+_(18g)	0.85916	0.00000	0.00000
254	IKZF1	IKZF1_+_-_(31g)	0.85384	0.00000	0.00000
256	IKZF1	IKZF1_extended_+_-_(31g)	0.85384	0.00000	0.00000
441	NKX6-2	NKX6-2_extended_+_-_(127g)	0.84669	0.00000	0.00000
439	NKX6-2	NKX6-2_+_-_(117g)	0.84500	0.00000	0.00000
466	PAX6	PAX6_extended_+_+_(163g)	0.83567	0.00000	0.00000
755	THRB	THRB_+_-_(112g)	0.83272	0.00000	0.00000
757	THRB	THRB_extended_+_-_(112g)	0.83272	0.00000	0.00000
13	ARNTL2	ARNTL2_extended_+_+_(10g)	0.83159	0.00000	0.00000
12	ARNTL2	ARNTL2_+_+_(10g)	0.83159	0.00000	0.00000
709	TAL1	TAL1_+_+_(99g)	0.83009	0.00000	0.00000
710	TAL1	TAL1_extended_+_+_(103g)	0.82871	0.00000	0.00000
334	LHX6	LHX6_extended_+_-_(32g)	0.82743	0.00000	0.00000
465	PAX6	PAX6_+_+_(40g)	0.82422	0.00000	0.00000
596	SOX10	SOX10_+_+_(209g)	0.81895	0.00000	0.00000
598	SOX10	SOX10_extended_+_+_(209g)	0.81895	0.00000	0.00000
200	FOXO1	FOXO1_extended_+_+_(97g)	0.81784	0.00000	0.00000
197	FOXO1	FOXO1_+_+_(97g)	0.81784	0.00000	0.00000
18	ARX	ARX_+_+_(54g)	0.81727	0.00000	0.00000
393	NEUROD2	NEUROD2_extended_+_+_(46g)	0.81676	0.00000	0.00000
25	ASCL1	ASCL1_extended_+_-_(76g)	0.81562	0.00000	0.00000
467	PAX6	PAX6_extended_+_-_(18g)	0.81378	0.00000	0.00000
462	OLIG2	OLIG2_extended_+_-_(46g)	0.81113	0.00000	0.00000
460	OLIG2	OLIG2_+_-_(46g)	0.81113	0.00000	0.00000
333	LHX6	LHX6_extended_+_+_(131g)	0.81009	0.00000	0.00000
331	LHX6	LHX6_+_+_(60g)	0.80462	0.00000	0.00000
518	PRRX1	PRRX1_extended_+_+_(220g)	0.80268	0.00000	0.00000
515	PRRX1	PRRX1_+_+_(207g)	0.80255	0.00000	0.00000
664	SOX9	SOX9_extended_+_-_(229g)	0.79496	0.00000	0.00000
194	FOXN2	FOXN2_extended_+_+_(70g)	0.79484	0.00000	0.00000
661	SOX9	SOX9_+_-_(224g)	0.79432	0.00000	0.00000
4	ADARB1	ADARB1_+_+_(20g)	0.79315	0.00000	0.00000
5	ADARB1	ADARB1_extended_+_+_(20g)	0.79315	0.00000	0.00000
392	NEUROD2	NEUROD2_+_+_(38g)	0.79257	0.00000	0.00000
635	SOX4	SOX4_extended_+_-_(151g)	0.79080	0.00000	0.00000
660	SOX9	SOX9_+_+_(58g)	0.78950	0.00000	0.00000
632	SOX4	SOX4_+_-_(145g)	0.78767	0.00000	0.00000
663	SOX9	SOX9_extended_+_+_(60g)	0.78753	0.00000	0.00000
21	ASCL1	ASCL1_+_+_(75g)	0.78552	0.00000	0.00000
22	ASCL1	ASCL1_+_-_(50g)	0.77859	0.00000	0.00000
121	ELF1	ELF1_extended_+_+_(264g)	0.77623	0.00000	0.00000
117	ELF1	ELF1_+_+_(263g)	0.77552	0.00000	0.00000
198	FOXO1	FOXO1_+_-_(148g)	0.77109	0.00000	0.00000
143	EMX1	EMX1_extended_+_-_(34g)	0.76930	0.00000	0.00000
201	FOXO1	FOXO1_extended_+_-_(146g)	0.76864	0.00000	0.00000
389	NCALD	NCALD_extended_+_-_(19g)	0.76585	0.00000	0.00000
388	NCALD	NCALD_+_-_(19g)	0.76585	0.00000	0.00000
73	CREB5	CREB5_extended_+_+_(29g)	0.76343	0.00000	0.00000
72	CREB5	CREB5_+_+_(29g)	0.76343	0.00000	0.00000
381	MYEF2	MYEF2_+_+_(63g)	0.76109	0.00000	0.00000
24	ASCL1	ASCL1_extended_+_+_(100g)	0.75666	0.00000	0.00000
696	STAT4	STAT4_extended_+_+_(21g)	0.75615	0.00000	0.00000
695	STAT4	STAT4_+_+_(21g)	0.75615	0.00000	0.00000
382	MYEF2	MYEF2_extended_+_+_(645g)	0.75053	0.00000	0.00000
329	LHX2	LHX2_+_+_(275g)	0.75016	0.00000	0.00000
542	REL	REL_extended_+_+_(133g)	0.74990	0.00000	0.00000
180	FOSL2	FOSL2_extended_+_-_(44g)	0.74966	0.00000	0.00000
330	LHX2	LHX2_extended_+_+_(302g)	0.74854	0.00000	0.00000
176	FOSB	FOSB_+_-_(51g)	0.74768	0.00000	0.00000
178	FOSB	FOSB_extended_+_-_(51g)	0.74768	0.00000	0.00000
296	KLF12	KLF12_extended_+_+_(138g)	0.74734	0.00000	0.00000
734	TCF7L1	TCF7L1_extended_+_-_(16g)	0.74084	0.00000	0.00000
733	TCF7L1	TCF7L1_+_-_(15g)	0.73833	0.00000	0.00000
794	ZEB1	ZEB1_extended_+_+_(19g)	0.73368	0.00000	0.00000
793	ZEB1	ZEB1_+_+_(19g)	0.73368	0.00000	0.00000
438	NKX6-2	NKX6-2_+_+_(25g)	0.73318	0.00000	0.00000
440	NKX6-2	NKX6-2_extended_+_+_(26g)	0.72926	0.00000	0.00000
237	HES1	HES1_extended_+_-_(38g)	0.72513	0.00000	0.00000
179	FOSL2	FOSL2_+_-_(33g)	0.72469	0.00000	0.00000
142	EMX1	EMX1_+_-_(25g)	0.72438	0.00000	0.00000
735	TCF7L2	TCF7L2_+_+_(84g)	0.71966	0.00000	0.00000
738	TCF7L2	TCF7L2_extended_+_+_(88g)	0.71907	0.00000	0.00000
277	JUNB	JUNB_+_-_(80g)	0.71763	0.00000	0.00000
602	SOX11	SOX11_extended_+_-_(120g)	0.71719	0.00000	0.00000
186	FOXG1	FOXG1_extended_+_+_(36g)	0.71560	0.00000	0.00000
332	LHX6	LHX6_+_-_(35g)	0.71345	0.00000	0.00000
600	SOX11	SOX11_+_-_(89g)	0.71165	0.00000	0.00000
181	FOS	FOS_+_-_(126g)	0.71158	0.00000	0.00000
183	FOS	FOS_extended_+_-_(126g)	0.71158	0.00000	0.00000
325	LARP1	LARP1_+_+_(114g)	0.70879	0.00000	0.00000
326	LARP1	LARP1_extended_+_+_(114g)	0.70879	0.00000	0.00000
532	RARB	RARB_extended_+_+_(100g)	0.70495	0.00000	0.00000
531	RARB	RARB_+_+_(100g)	0.70495	0.00000	0.00000
279	JUNB	JUNB_extended_+_-_(84g)	0.70189	0.00000	0.00000
547	RFX1	RFX1_extended_+_+_(364g)	0.70040	0.00000	0.00000
507	POU6F2	POU6F2_extended_+_+_(309g)	0.69929	0.00000	0.00000
506	POU6F2	POU6F2_+_+_(309g)	0.69929	0.00000	0.00000
185	FOXG1	FOXG1_+_+_(17g)	0.69909	0.00000	0.00000
657	SOX8	SOX8_extended_+_+_(104g)	0.69230	0.00000	0.00000
654	SOX8	SOX8_+_+_(96g)	0.69045	0.00000	0.00000
739	TCF7L2	TCF7L2_extended_+_-_(57g)	0.68803	0.00000	0.00000
736	TCF7L2	TCF7L2_+_-_(48g)	0.68551	0.00000	0.00000
545	RFX1	RFX1_+_+_(326g)	0.68514	0.00000	0.00000
30	ATF3	ATF3_extended_+_+_(77g)	0.68467	0.00000	0.00000
648	SOX6	SOX6_extended_+_-_(440g)	0.68271	0.00000	0.00000
644	SOX6	SOX6_+_-_(488g)	0.68187	0.00000	0.00000
310	KLF4	KLF4_+_+_(28g)	0.68007	0.00000	0.00000
311	KLF4	KLF4_extended_+_+_(28g)	0.68007	0.00000	0.00000
576	RXRA	RXRA_+_+_(308g)	0.67837	0.00000	0.00000
578	RXRA	RXRA_extended_+_+_(308g)	0.67837	0.00000	0.00000
29	ATF3	ATF3_+_+_(74g)	0.67772	0.00000	0.00000
90	DLX1	DLX1_+_-_(26g)	0.67206	0.00000	0.00000
617	SOX1	SOX1_extended_+_+_(47g)	0.67151	0.00000	0.00000
162	ETV1	ETV1_extended_+_+_(61g)	0.66922	0.00000	0.00000
92	DLX1	DLX1_extended_+_-_(32g)	0.66883	0.00000	0.00000
555	RFX3	RFX3_extended_+_+_(686g)	0.66633	0.00000	0.00000
565	RFX8	RFX8_extended_+_+_(147g)	0.66548	0.00000	0.00000
553	RFX3	RFX3_+_+_(561g)	0.66377	0.00000	0.00000
713	TCF12	TCF12_+_+_(665g)	0.66312	0.00000	0.00000
717	TCF12	TCF12_extended_+_+_(693g)	0.66284	0.00000	0.00000
368	MEIS2	MEIS2_extended_+_+_(22g)	0.65962	0.00000	0.00000
579	RXRA	RXRA_extended_+_-_(150g)	0.65410	0.00000	0.00000
577	RXRA	RXRA_+_-_(150g)	0.65410	0.00000	0.00000
91	DLX1	DLX1_extended_+_+_(129g)	0.65253	0.00000	0.00000
89	DLX1	DLX1_+_+_(127g)	0.65177	0.00000	0.00000
725	TCF4	TCF4_+_+_(551g)	0.64794	0.00000	0.00000
175	FOSB	FOSB_+_+_(57g)	0.64500	0.00000	0.00000
177	FOSB	FOSB_extended_+_+_(57g)	0.64500	0.00000	0.00000
390	NEUROD1	NEUROD1_+_+_(66g)	0.64294	0.00000	0.00000
391	NEUROD1	NEUROD1_extended_+_+_(66g)	0.64294	0.00000	0.00000
729	TCF4	TCF4_extended_+_+_(783g)	0.64190	0.00000	0.00000
340	MAFF	MAFF_extended_+_+_(23g)	0.64131	0.00000	0.00000
339	MAFF	MAFF_+_+_(22g)	0.64126	0.00000	0.00000
163	ETV1	ETV1_extended_+_-_(47g)	0.64107	0.00000	0.00000
399	NFAT5	NFAT5_extended_+_+_(159g)	0.64058	0.00000	0.00000
396	NFAT5	NFAT5_+_+_(159g)	0.64058	0.00000	0.00000
238	HES4	HES4_extended_+_-_(10g)	0.63934	0.00000	0.00000
313	KLF6	KLF6_+_+_(117g)	0.63888	0.00000	0.00000
314	KLF6	KLF6_extended_+_+_(117g)	0.63888	0.00000	0.00000
422	NFIB	NFIB_extended_+_+_(574g)	0.63562	0.00000	0.00000
421	NFIB	NFIB_+_+_(574g)	0.63562	0.00000	0.00000
118	ELF1	ELF1_+_-_(151g)	0.63526	0.00000	0.00000
584	SALL3	SALL3_extended_+_+_(10g)	0.63498	0.00000	0.00000
409	NFE2L2	NFE2L2_+_-_(98g)	0.63482	0.00000	0.00000
503	POU3F4	POU3F4_extended_+_+_(62g)	0.63478	0.00000	0.00000
549	RFX2	RFX2_+_-_(38g)	0.63441	0.00000	0.00000
350	MCTP2	MCTP2_extended_+_+_(66g)	0.63306	0.00000	0.00000
349	MCTP2	MCTP2_+_+_(66g)	0.63306	0.00000	0.00000
551	RFX2	RFX2_extended_+_-_(36g)	0.63241	0.00000	0.00000
492	POU2F1	POU2F1_+_+_(287g)	0.63192	0.00000	0.00000
122	ELF1	ELF1_extended_+_-_(156g)	0.63106	0.00000	0.00000
411	NFE2L2	NFE2L2_extended_+_-_(120g)	0.62626	0.00000	0.00000
509	PRDM1	PRDM1_+_+_(77g)	0.62545	0.00000	0.00000
510	PRDM1	PRDM1_extended_+_+_(77g)	0.62545	0.00000	0.00000
502	POU3F4	POU3F4_+_+_(53g)	0.62533	0.00000	0.00000
293	KLF12	KLF12_+_-_(50g)	0.62394	0.00000	0.00000
297	KLF12	KLF12_extended_+_-_(51g)	0.62384	0.00000	0.00000
714	TCF12	TCF12_+_-_(250g)	0.62001	0.00000	0.00000
718	TCF12	TCF12_extended_+_-_(265g)	0.61947	0.00000	0.00000
289	JUN	JUN_extended_+_-_(64g)	0.61710	0.00000	0.00000
112	EGR4	EGR4_extended_+_+_(187g)	0.61644	0.00000	0.00000
158	ETS2	ETS2_+_+_(283g)	0.61288	0.00000	0.00000
159	ETS2	ETS2_extended_+_+_(284g)	0.61285	0.00000	0.00000
631	SOX4	SOX4_+_+_(57g)	0.60937	0.00000	0.00000
161	ETV1	ETV1_+_-_(34g)	0.60873	0.00000	0.00000
826	ZNF536	ZNF536_+_-_(23g)	0.60551	0.00000	0.00000
828	ZNF536	ZNF536_extended_+_-_(23g)	0.60551	0.00000	0.00000
169	ETV6	ETV6_+_-_(42g)	0.60227	0.00000	0.00000
287	JUN	JUN_+_-_(68g)	0.60043	0.00000	0.00000
160	ETV1	ETV1_+_+_(44g)	0.60034	0.00000	0.00000
417	NFIA	NFIA_extended_+_+_(483g)	0.59964	0.00000	0.00000
413	NFIA	NFIA_+_+_(483g)	0.59964	0.00000	0.00000
111	EGR4	EGR4_+_+_(135g)	0.59786	0.00000	0.00000
516	PRRX1	PRRX1_+_-_(31g)	0.59786	0.00000	0.00000
407	NFE2L1	NFE2L1_extended_+_+_(334g)	0.59390	0.00000	0.00000
634	SOX4	SOX4_extended_+_+_(72g)	0.59381	0.00000	0.00000
473	PBX3	PBX3_extended_+_+_(128g)	0.59353	0.00000	0.00000
472	PBX3	PBX3_+_+_(128g)	0.59353	0.00000	0.00000
618	SOX1	SOX1_extended_+_-_(116g)	0.59338	0.00000	0.00000
338	MAFB	MAFB_extended_+_-_(12g)	0.59291	0.00000	0.00000
787	ZBTB7A	ZBTB7A_+_+_(32g)	0.59184	0.00000	0.00000
789	ZBTB7A	ZBTB7A_extended_+_+_(32g)	0.59184	0.00000	0.00000
406	NFE2L1	NFE2L1_+_+_(313g)	0.58886	0.00000	0.00000
79	CUX1	CUX1_+_+_(247g)	0.58873	0.00000	0.00000
82	CUX1	CUX1_extended_+_+_(247g)	0.58873	0.00000	0.00000
760	TPPP	TPPP_extended_+_+_(64g)	0.58850	0.00000	0.00000
758	TPPP	TPPP_+_+_(64g)	0.58850	0.00000	0.00000
519	PRRX1	PRRX1_extended_+_-_(33g)	0.58823	0.00000	0.00000
196	FOXN3	FOXN3_extended_+_+_(71g)	0.58527	0.00000	0.00000
676	SPI1	SPI1_+_+_(213g)	0.58211	0.00000	0.00000
678	SPI1	SPI1_extended_+_+_(219g)	0.58112	0.00000	0.00000
620	SOX2	SOX2_+_+_(52g)	0.58048	0.00000	0.00000
764	TRPS1	TRPS1_extended_+_+_(16g)	0.57907	0.00000	0.00000
624	SOX2	SOX2_extended_+_-_(111g)	0.57718	0.00000	0.00000
679	SPI1	SPI1_extended_+_-_(38g)	0.57663	0.00000	0.00000
337	MAFB	MAFB_+_-_(11g)	0.57585	0.00000	0.00000
781	ZBTB18	ZBTB18_+_-_(150g)	0.57548	0.00000	0.00000
623	SOX2	SOX2_extended_+_+_(54g)	0.57402	0.00000	0.00000
616	SOX1	SOX1_+_-_(103g)	0.57316	0.00000	0.00000
15	ARNTL	ARNTL_extended_+_+_(18g)	0.56042	0.00000	0.00000
96	DLX6	DLX6_extended_+_+_(36g)	0.56036	0.00000	0.00000
782	ZBTB18	ZBTB18_extended_+_-_(172g)	0.55973	0.00000	0.00000
493	POU2F1	POU2F1_extended_+_+_(456g)	0.55929	0.00000	0.00000
414	NFIA	NFIA_+_-_(255g)	0.55911	0.00000	0.00000
418	NFIA	NFIA_extended_+_-_(255g)	0.55911	0.00000	0.00000
640	SOX5	SOX5_extended_+_+_(91g)	0.55762	0.00000	0.00000
446	NR2E1	NR2E1_+_+_(62g)	0.55339	0.00000	0.00000
601	SOX11	SOX11_extended_+_+_(21g)	0.55224	0.00000	0.00000
608	SOX13	SOX13_extended_+_-_(42g)	0.55206	0.00000	0.00000
742	TEAD1	TEAD1_extended_+_+_(265g)	0.54845	0.00000	0.00000
740	TEAD1	TEAD1_+_+_(265g)	0.54845	0.00000	0.00000
14	ARNTL	ARNTL_+_+_(23g)	0.54649	0.00000	0.00000
95	DLX6	DLX6_+_+_(33g)	0.54551	0.00000	0.00000
312	KLF5	KLF5_extended_+_-_(28g)	0.54289	0.00000	0.00000
170	ETV6	ETV6_extended_+_+_(366g)	0.54179	0.00000	0.00000
369	MEIS2	MEIS2_extended_+_-_(67g)	0.53723	0.00000	0.00000
769	VSX1	VSX1_+_+_(38g)	0.53444	0.00000	0.00000
770	VSX1	VSX1_extended_+_+_(38g)	0.53444	0.00000	0.00000
168	ETV6	ETV6_+_+_(358g)	0.53125	0.00000	0.00000
425	NFIC	NFIC_extended_+_+_(911g)	0.52577	0.00000	0.00000
423	NFIC	NFIC_+_+_(911g)	0.52577	0.00000	0.00000
408	NFE2L1	NFE2L1_extended_+_-_(21g)	0.52570	0.00000	0.00000
187	FOXG1	FOXG1_extended_+_-_(122g)	0.52558	0.00000	0.00000
522	PRRX2	PRRX2_extended_+_+_(21g)	0.52451	0.00000	0.00000
521	PRRX2	PRRX2_+_+_(21g)	0.52451	0.00000	0.00000
249	HOXD1	HOXD1_+_-_(20g)	0.52146	0.00000	0.00000
795	ZEB2	ZEB2_+_-_(21g)	0.51567	0.00000	0.00000
797	ZEB2	ZEB2_extended_+_-_(21g)	0.51567	0.00000	0.00000
431	NFIX	NFIX_extended_+_-_(499g)	0.51384	0.00000	0.00000
428	NFIX	NFIX_+_-_(499g)	0.51384	0.00000	0.00000
525	PURA	PURA_extended_+_-_(203g)	0.51219	0.00000	0.00000
523	PURA	PURA_+_-_(203g)	0.51219	0.00000	0.00000
447	NR2E1	NR2E1_extended_+_+_(68g)	0.51127	0.00000	0.00000
765	VEZF1	VEZF1_+_-_(54g)	0.51100	0.00000	0.00000
767	VEZF1	VEZF1_extended_+_-_(54g)	0.51100	0.00000	0.00000
726	TCF4	TCF4_+_-_(310g)	0.50194	0.00000	0.00000
730	TCF4	TCF4_extended_+_-_(359g)	0.49968	0.00000	0.00000
251	HOXD1	HOXD1_extended_+_-_(25g)	0.49833	0.00000	0.00000
58	CEBPD	CEBPD_extended_+_+_(47g)	0.49753	0.00000	0.00000
57	CEBPD	CEBPD_+_+_(47g)	0.49753	0.00000	0.00000
595	SMARCC2	SMARCC2_extended_+_+_(53g)	0.49727	0.00000	0.00000
594	SMARCC2	SMARCC2_+_+_(53g)	0.49727	0.00000	0.00000
211	FOXP2	FOXP2_extended_+_+_(36g)	0.49687	0.00000	0.00000
209	FOXP2	FOXP2_+_+_(36g)	0.49687	0.00000	0.00000
607	SOX13	SOX13_extended_+_+_(37g)	0.49666	0.00000	0.00000
677	SPI1	SPI1_+_-_(17g)	0.49036	0.00000	0.00000
241	HMX1	HMX1_+_+_(30g)	0.48301	0.00000	0.00000
242	HMX1	HMX1_extended_+_+_(30g)	0.48301	0.00000	0.00000
35	BACH1	BACH1_extended_+_-_(85g)	0.48004	0.00000	0.00000
32	ATF7	ATF7_extended_+_+_(18g)	0.47959	0.00000	0.00000
243	HNF4G	HNF4G_+_+_(12g)	0.47956	0.00000	0.00000
245	HNF4G	HNF4G_extended_+_+_(12g)	0.47956	0.00000	0.00000
823	ZNF467	ZNF467_+_+_(38g)	0.47614	0.00000	0.00000
128	ELF2	ELF2_extended_+_-_(302g)	0.46920	0.00000	0.00000
20	AR	AR_extended_+_-_(35g)	0.46873	0.00000	0.00000
19	AR	AR_+_-_(35g)	0.46873	0.00000	0.00000
31	ATF7	ATF7_+_+_(16g)	0.46640	0.00000	0.00000
605	SOX13	SOX13_+_+_(35g)	0.46499	0.00000	0.00000
234	HDAC2	HDAC2_extended_+_-_(39g)	0.46461	0.00000	0.00000
232	HDAC2	HDAC2_+_-_(39g)	0.46461	0.00000	0.00000
148	EP300	EP300_extended_+_+_(925g)	0.46396	0.00000	0.00000
147	EP300	EP300_+_+_(925g)	0.46396	0.00000	0.00000
543	REST	REST_+_+_(30g)	0.46342	0.00000	0.00000
125	ELF2	ELF2_+_-_(278g)	0.46034	0.00000	0.00000
152	ERG	ERG_extended_+_+_(43g)	0.45929	0.00000	0.00000
427	NFIX	NFIX_+_+_(210g)	0.45866	0.00000	0.00000
430	NFIX	NFIX_extended_+_+_(210g)	0.45866	0.00000	0.00000
171	ETV6	ETV6_extended_+_-_(57g)	0.45857	0.00000	0.00000
544	REST	REST_extended_+_+_(43g)	0.45763	0.00000	0.00000
228	HBP1	HBP1_extended_+_+_(203g)	0.45642	0.00000	0.00000
400	NFAT5	NFAT5_extended_+_-_(64g)	0.45562	0.00000	0.00000
397	NFAT5	NFAT5_+_-_(64g)	0.45562	0.00000	0.00000
141	EMX1	EMX1_+_+_(17g)	0.45494	0.00000	0.00000
300	KLF13	KLF13_+_-_(80g)	0.45488	0.00000	0.00000
301	KLF13	KLF13_extended_+_-_(80g)	0.45488	0.00000	0.00000
227	HBP1	HBP1_+_+_(173g)	0.45474	0.00000	0.00000
495	POU2F2	POU2F2_extended_+_+_(75g)	0.45437	0.00000	0.00000
306	KLF2	KLF2_+_+_(17g)	0.45384	0.00000	0.00000
307	KLF2	KLF2_extended_+_+_(17g)	0.45384	0.00000	0.00000
151	ERG	ERG_+_+_(40g)	0.45136	0.00000	0.00000
831	ZNF736	ZNF736_+_+_(61g)	0.45110	0.00000	0.00000
376	MSX1	MSX1_extended_+_+_(76g)	0.44952	0.00000	0.00000
374	MSX1	MSX1_+_+_(72g)	0.44695	0.00000	0.00000
157	ETS1	ETS1_extended_+_-_(143g)	0.44398	0.00000	0.00000
452	NRF1	NRF1_+_+_(299g)	0.44383	0.00000	0.00000
155	ETS1	ETS1_+_-_(142g)	0.44318	0.00000	0.00000
599	SOX10	SOX10_extended_+_-_(37g)	0.43823	0.00000	0.00000
597	SOX10	SOX10_+_-_(37g)	0.43823	0.00000	0.00000
93	DLX5	DLX5_+_+_(36g)	0.43642	0.00000	0.00000
94	DLX5	DLX5_extended_+_+_(36g)	0.43642	0.00000	0.00000
651	SOX7	SOX7_+_-_(46g)	0.43409	0.00000	0.00000
10	ARID3A	ARID3A_+_-_(90g)	0.42895	0.00000	0.00000
448	NR3C1	NR3C1_+_+_(264g)	0.42800	0.00000	0.00000
450	NR3C1	NR3C1_extended_+_+_(264g)	0.42800	0.00000	0.00000
133	ELF4	ELF4_extended_+_+_(45g)	0.42619	0.00000	0.00000
653	SOX7	SOX7_extended_+_-_(47g)	0.42523	0.00000	0.00000
44	BCLAF1	BCLAF1_+_-_(114g)	0.42424	0.00000	0.00000
46	BCLAF1	BCLAF1_extended_+_-_(114g)	0.42424	0.00000	0.00000
131	ELF4	ELF4_+_+_(42g)	0.42320	0.00000	0.00000
606	SOX13	SOX13_+_-_(19g)	0.42314	0.00000	0.00000
480	PKM	PKM_+_-_(59g)	0.42207	0.00000	0.00000
482	PKM	PKM_extended_+_-_(59g)	0.42207	0.00000	0.00000
208	FOXP1	FOXP1_extended_+_-_(113g)	0.41988	0.00000	0.00000
357	MEF2A	MEF2A_extended_+_-_(258g)	0.41859	0.00000	0.00000
207	FOXP1	FOXP1_+_-_(122g)	0.41837	0.00000	0.00000
615	SOX1	SOX1_+_+_(10g)	0.41787	0.00000	0.00000
99	E2F3	E2F3_+_-_(39g)	0.41559	0.00000	0.00000
536	RB1	RB1_extended_+_-_(67g)	0.41445	0.00000	0.00000
534	RB1	RB1_+_-_(67g)	0.41445	0.00000	0.00000
586	SIX5	SIX5_+_+_(15g)	0.41154	0.00000	0.00000
588	SIX5	SIX5_extended_+_+_(15g)	0.41154	0.00000	0.00000
166	ETV5	ETV5_+_+_(198g)	0.40552	0.00000	0.00000
167	ETV5	ETV5_extended_+_+_(198g)	0.40552	0.00000	0.00000
47	BDP1	BDP1_+_-_(33g)	0.40526	0.00000	0.00000
355	MEF2A	MEF2A_+_-_(233g)	0.40502	0.00000	0.00000
484	PLAGL1	PLAGL1_+_+_(40g)	0.40324	0.00000	0.00000
485	PLAGL1	PLAGL1_extended_+_+_(40g)	0.40324	0.00000	0.00000
682	SREBF1	SREBF1_+_+_(102g)	0.40308	0.00000	0.00000
683	SREBF1	SREBF1_extended_+_+_(102g)	0.40308	0.00000	0.00000
275	JDP2	JDP2_extended_+_-_(72g)	0.40234	0.00000	0.00000
824	ZNF467	ZNF467_extended_+_+_(21g)	0.40015	0.00000	0.00000
33	BACH1	BACH1_+_-_(79g)	0.39582	0.00000	0.00000
273	JDP2	JDP2_+_-_(74g)	0.39466	0.00000	0.00000
593	SMARCC1	SMARCC1_extended_+_+_(351g)	0.39405	0.00000	0.00000
592	SMARCC1	SMARCC1_+_+_(351g)	0.39405	0.00000	0.00000
250	HOXD1	HOXD1_extended_+_+_(9g)	0.38419	0.00000	0.00000
513	PRNP	PRNP_extended_+_+_(21g)	0.38037	0.00000	0.00000
537	RBBP5	RBBP5_+_-_(52g)	0.37882	0.00000	0.00000
538	RBBP5	RBBP5_extended_+_-_(52g)	0.37882	0.00000	0.00000
156	ETS1	ETS1_extended_+_+_(155g)	0.37597	0.00000	0.00000
154	ETS1	ETS1_+_+_(153g)	0.37577	0.00000	0.00000
759	TPPP	TPPP_+_-_(45g)	0.37268	0.00000	0.00000
761	TPPP	TPPP_extended_+_-_(45g)	0.37268	0.00000	0.00000
684	SREBF2	SREBF2_+_-_(136g)	0.37245	0.00000	0.00000
685	SREBF2	SREBF2_extended_+_-_(136g)	0.37245	0.00000	0.00000
681	SPIB	SPIB_extended_+_+_(78g)	0.37156	0.00000	0.00000
680	SPIB	SPIB_+_+_(78g)	0.37156	0.00000	0.00000
585	SF1	SF1_extended_+_-_(15g)	0.37146	0.00000	0.00000
449	NR3C1	NR3C1_+_-_(108g)	0.37145	0.00000	0.00000
377	MSX1	MSX1_extended_+_-_(16g)	0.36819	0.00000	0.00000
100	E2F3	E2F3_extended_+_-_(50g)	0.36560	0.00000	0.00000
48	BDP1	BDP1_extended_+_-_(35g)	0.35797	0.00000	0.00000
773	YY1	YY1_+_+_(155g)	0.35785	0.00000	0.00000
701	STAT6	STAT6_+_+_(90g)	0.35706	0.00000	0.00000
702	STAT6	STAT6_extended_+_+_(90g)	0.35706	0.00000	0.00000
658	SOX8	SOX8_extended_+_-_(60g)	0.35624	0.00000	0.00000
375	MSX1	MSX1_+_-_(14g)	0.35621	0.00000	0.00000
153	ERG	ERG_extended_+_-_(12g)	0.35490	0.00000	0.00000
655	SOX8	SOX8_+_-_(55g)	0.34885	0.00000	0.00000
11	ARID3A	ARID3A_extended_+_-_(90g)	0.34422	0.00000	0.00000
587	SIX5	SIX5_+_-_(9g)	0.34351	0.00000	0.00000
589	SIX5	SIX5_extended_+_-_(9g)	0.34351	0.00000	0.00000
805	ZNF148	ZNF148_+_-_(73g)	0.34294	0.00000	0.00000
806	ZNF148	ZNF148_extended_+_-_(73g)	0.34294	0.00000	0.00000
527	RAD21	RAD21_+_-_(89g)	0.34052	0.00000	0.00000
528	RAD21	RAD21_extended_+_-_(89g)	0.34052	0.00000	0.00000
264	IRF2	IRF2_+_-_(143g)	0.33933	0.00000	0.00000
266	IRF2	IRF2_extended_+_-_(169g)	0.33566	0.00000	0.00000
108	EGR1	EGR1_extended_+_-_(27g)	0.33211	0.00000	0.00000
86	DBX2	DBX2_extended_+_+_(42g)	0.33207	0.00000	0.00000
435	NHLH2	NHLH2_+_+_(12g)	0.32727	0.00000	0.00000
53	CCDC25	CCDC25_+_-_(26g)	0.32682	0.00000	0.00000
54	CCDC25	CCDC25_extended_+_-_(26g)	0.32682	0.00000	0.00000
49	BRF2	BRF2_+_+_(35g)	0.32549	0.00000	0.00000
51	BRF2	BRF2_extended_+_+_(35g)	0.32549	0.00000	0.00000
318	KLF7	KLF7_extended_+_-_(27g)	0.32486	0.00000	0.00000
775	YY1	YY1_extended_+_+_(144g)	0.31961	0.00000	0.00000
55	CEBPB	CEBPB_+_-_(57g)	0.31899	0.00000	0.00000
56	CEBPB	CEBPB_extended_+_-_(58g)	0.31867	0.00000	0.00000
687	STAT1	STAT1_+_+_(180g)	0.31709	0.00000	0.00000
689	STAT1	STAT1_extended_+_+_(180g)	0.31709	0.00000	0.00000
80	CUX1	CUX1_+_-_(107g)	0.31404	0.00000	0.00000
83	CUX1	CUX1_extended_+_-_(107g)	0.31404	0.00000	0.00000
316	KLF7	KLF7_+_-_(20g)	0.30764	0.00000	0.00000
741	TEAD1	TEAD1_+_-_(31g)	0.30399	0.00000	0.00000
743	TEAD1	TEAD1_extended_+_-_(31g)	0.30399	0.00000	0.00000
629	SOX3	SOX3_extended_+_+_(11g)	0.30270	0.00000	0.00000
627	SOX3	SOX3_+_+_(11g)	0.30270	0.00000	0.00000
75	CTCF	CTCF_+_-_(148g)	0.29659	0.00000	0.00000
652	SOX7	SOX7_extended_+_+_(7g)	0.29491	0.00000	0.00000
394	NF1	NF1_+_-_(354g)	0.29469	0.00000	0.00000
395	NF1	NF1_extended_+_-_(354g)	0.29469	0.00000	0.00000
785	ZBTB20	ZBTB20_extended_+_+_(33g)	0.29460	0.00000	0.00000
783	ZBTB20	ZBTB20_+_+_(33g)	0.29460	0.00000	0.00000
498	POU3F1	POU3F1_extended_+_+_(40g)	0.29036	0.00000	0.00000
496	POU3F1	POU3F1_+_+_(40g)	0.29036	0.00000	0.00000
351	MDM2	MDM2_+_+_(11g)	0.29020	0.00000	0.00000
352	MDM2	MDM2_extended_+_+_(11g)	0.29020	0.00000	0.00000
52	BRF2	BRF2_extended_+_-_(17g)	0.28911	0.00000	0.00000
50	BRF2	BRF2_+_-_(17g)	0.28911	0.00000	0.00000
694	STAT3	STAT3_extended_+_-_(118g)	0.28774	0.00000	0.00000
693	STAT3	STAT3_+_-_(118g)	0.28774	0.00000	0.00000
66	CIC	CIC_extended_+_+_(12g)	0.28630	0.00000	0.00000
707	TAF7	TAF7_extended_+_+_(27g)	0.28496	0.00000	0.00000
705	TAF7	TAF7_+_+_(27g)	0.28496	0.00000	0.00000
165	ETV4	ETV4_extended_+_+_(42g)	0.27765	0.00000	0.00000
491	POLR3G	POLR3G_extended_+_+_(21g)	0.27607	0.00000	0.00000
490	POLR3G	POLR3G_+_+_(21g)	0.27607	0.00000	0.00000
535	RB1	RB1_extended_+_+_(30g)	0.27499	0.00000	0.00000
533	RB1	RB1_+_+_(30g)	0.27499	0.00000	0.00000
566	RFXANK	RFXANK_+_-_(21g)	0.27473	0.00000	0.00000
567	RFXANK	RFXANK_extended_+_-_(21g)	0.27473	0.00000	0.00000
366	MEF2D	MEF2D_extended_+_+_(185g)	0.27472	0.00000	0.00000
373	MEOX2	MEOX2_extended_+_+_(38g)	0.27385	0.00000	0.00000
224	GPANK1	GPANK1_extended_+_-_(18g)	0.27359	0.00000	0.00000
222	GPANK1	GPANK1_+_-_(18g)	0.27359	0.00000	0.00000
231	HDAC2	HDAC2_+_+_(22g)	0.27310	0.00000	0.00000
233	HDAC2	HDAC2_extended_+_+_(22g)	0.27310	0.00000	0.00000
302	KLF16	KLF16_+_+_(11g)	0.27034	0.00000	0.00000
668	SP1	SP1_extended_+_+_(171g)	0.26329	0.00000	0.00000
372	MEOX2	MEOX2_+_+_(43g)	0.26053	0.00000	0.00000
138	ELK3	ELK3_extended_+_+_(57g)	0.25821	0.00000	0.00000
137	ELK3	ELK3_+_+_(56g)	0.25819	0.00000	0.00000
650	SOX7	SOX7_+_+_(5g)	0.25722	0.00000	0.00000
149	ERF	ERF_+_+_(48g)	0.25494	0.00000	0.00000
150	ERF	ERF_extended_+_+_(48g)	0.25494	0.00000	0.00000
453	NRF1	NRF1_+_-_(191g)	0.25493	0.00000	0.00000
581	RXRB	RXRB_+_-_(10g)	0.25350	0.00000	0.00000
583	RXRB	RXRB_extended_+_-_(10g)	0.25350	0.00000	0.00000
60	CHD2	CHD2_+_-_(38g)	0.24467	0.00000	0.00000
613	SOX15	SOX15_extended_+_-_(41g)	0.24385	0.00000	0.00000
610	SOX15	SOX15_+_-_(41g)	0.24385	0.00000	0.00000
37	BACH2	BACH2_+_-_(123g)	0.24160	0.00000	0.00000
637	SOX5	SOX5_+_+_(72g)	0.23907	0.00000	0.00000
541	RELB	RELB_extended_+_+_(57g)	0.23885	0.00000	0.00000
819	ZNF3	ZNF3_extended_+_+_(12g)	0.23632	0.00000	0.00000
817	ZNF3	ZNF3_+_+_(12g)	0.23632	0.00000	0.00000
348	MAZ	MAZ_extended_+_+_(195g)	0.23502	0.00000	0.00000
347	MAZ	MAZ_+_+_(195g)	0.23502	0.00000	0.00000
788	ZBTB7A	ZBTB7A_+_-_(46g)	0.23473	0.00000	0.00000
790	ZBTB7A	ZBTB7A_extended_+_-_(46g)	0.23473	0.00000	0.00000
39	BACH2	BACH2_extended_+_-_(124g)	0.23418	0.00000	0.00000
77	CTCF	CTCF_extended_+_-_(135g)	0.23296	0.00000	0.00000
745	TEF	TEF_extended_+_+_(19g)	0.23066	0.00000	0.00000
630	SOX3	SOX3_extended_+_-_(9g)	0.23048	0.00000	0.00000
628	SOX3	SOX3_+_-_(9g)	0.23048	0.00000	0.00000
469	PAX7	PAX7_+_-_(13g)	0.22623	0.00000	0.00000
470	PAX7	PAX7_extended_+_-_(13g)	0.22623	0.00000	0.00000
454	NRF1	NRF1_extended_+_-_(217g)	0.22398	0.00000	0.00000
744	TEF	TEF_+_+_(10g)	0.22370	0.00000	0.00000
365	MEF2D	MEF2D_+_+_(175g)	0.22192	0.00000	0.00000
489	POLR2A	POLR2A_extended_+_-_(154g)	0.22148	0.00000	0.00000
487	POLR2A	POLR2A_+_-_(154g)	0.22148	0.00000	0.00000
771	YBX1	YBX1_+_-_(21g)	0.22105	0.00000	0.00000
772	YBX1	YBX1_extended_+_-_(21g)	0.22105	0.00000	0.00000
746	TFAP2C	TFAP2C_+_-_(9g)	0.21832	0.00000	0.00000
747	TFAP2C	TFAP2C_extended_+_-_(9g)	0.21832	0.00000	0.00000
379	MXI1	MXI1_+_-_(86g)	0.21665	0.00000	0.00000
380	MXI1	MXI1_extended_+_-_(86g)	0.21665	0.00000	0.00000
62	CHURC1	CHURC1_+_+_(143g)	0.21403	0.00000	0.00000
64	CHURC1	CHURC1_extended_+_+_(143g)	0.21403	0.00000	0.00000
456	ODC1	ODC1_extended_+_+_(55g)	0.21388	0.00000	0.00000
455	ODC1	ODC1_+_+_(55g)	0.21388	0.00000	0.00000
451	NR3C1	NR3C1_extended_+_-_(81g)	0.21109	0.00000	0.00000
164	ETV4	ETV4_+_+_(39g)	0.21064	0.00000	0.00000
666	SP1	SP1_+_+_(134g)	0.20614	0.00000	0.00000
304	KLF16	KLF16_extended_+_+_(15g)	0.20596	0.00000	0.00000
508	PRDM11	PRDM11_extended_+_-_(23g)	0.20559	0.00000	0.00000
87	DIABLO	DIABLO_+_+_(23g)	0.20296	0.00000	0.00000
88	DIABLO	DIABLO_extended_+_+_(23g)	0.20296	0.00000	0.00000
6	AGGF1	AGGF1_+_+_(136g)	0.20108	0.00000	0.00000
8	AGGF1	AGGF1_extended_+_+_(136g)	0.20108	0.00000	0.00000
97	E2F1	E2F1_+_-_(20g)	0.19795	0.00000	0.00000
643	SOX6	SOX6_+_+_(73g)	0.19677	0.00000	0.00000
323	KLF9	KLF9_extended_+_+_(23g)	0.19598	0.00000	0.00000
321	KLF9	KLF9_+_+_(23g)	0.19598	0.00000	0.00000
403	NFATC3	NFATC3_+_-_(54g)	0.19381	0.00000	0.00000
405	NFATC3	NFATC3_extended_+_-_(54g)	0.19381	0.00000	0.00000
386	NANOS1	NANOS1_+_+_(10g)	0.19269	0.00000	0.00000
387	NANOS1	NANOS1_extended_+_+_(10g)	0.19269	0.00000	0.00000
98	E2F1	E2F1_extended_+_-_(23g)	0.19228	0.00000	0.00000
317	KLF7	KLF7_extended_+_+_(29g)	0.19219	0.00000	0.00000
291	KLF11	KLF11_+_+_(19g)	0.19192	0.00000	0.00000
292	KLF11	KLF11_extended_+_+_(19g)	0.19192	0.00000	0.00000
239	HESX1	HESX1_+_-_(13g)	0.18838	0.00000	0.00000
240	HESX1	HESX1_extended_+_-_(13g)	0.18838	0.00000	0.00000
753	THRA	THRA_extended_+_-_(50g)	0.18690	0.00000	0.00000
751	THRA	THRA_+_-_(50g)	0.18690	0.00000	0.00000
497	POU3F1	POU3F1_+_-_(15g)	0.18447	0.00000	0.00000
499	POU3F1	POU3F1_extended_+_-_(15g)	0.18447	0.00000	0.00000
105	E2F6	E2F6_extended_+_-_(14g)	0.18308	0.00000	0.00000
104	E2F6	E2F6_+_-_(14g)	0.18308	0.00000	0.00000
3	ACO1	ACO1_extended_+_-_(29g)	0.18298	0.00000	0.00000
1	ACO1	ACO1_+_-_(29g)	0.18298	0.00000	0.00000
647	SOX6	SOX6_extended_+_+_(83g)	0.18191	0.00000	0.00000
109	EGR2	EGR2_+_+_(27g)	0.18062	0.00000	0.00000
488	POLR2A	POLR2A_extended_+_+_(188g)	0.17979	0.00000	0.00000
486	POLR2A	POLR2A_+_+_(188g)	0.17979	0.00000	0.00000
791	ZBTB7B	ZBTB7B_+_+_(31g)	0.17942	0.00000	0.00000
792	ZBTB7B	ZBTB7B_extended_+_+_(31g)	0.17942	0.00000	0.00000
110	EGR2	EGR2_extended_+_+_(129g)	0.17878	0.00000	0.00000
59	CHD2	CHD2_+_+_(43g)	0.17273	0.00000	0.00000
61	CHD2	CHD2_extended_+_+_(45g)	0.17248	0.00000	0.00000
324	KLF9	KLF9_extended_+_-_(21g)	0.16874	0.00000	0.00000
322	KLF9	KLF9_+_-_(21g)	0.16874	0.00000	0.00000
335	LUZP2	LUZP2_+_-_(9g)	0.16854	0.00000	0.00000
336	LUZP2	LUZP2_extended_+_-_(9g)	0.16854	0.00000	0.00000
471	PBX1	PBX1_extended_+_-_(30g)	0.16735	0.00000	0.00000
778	ZBTB11	ZBTB11_extended_+_+_(45g)	0.15496	0.00000	0.00000
777	ZBTB11	ZBTB11_+_+_(45g)	0.15496	0.00000	0.00000
512	PRNP	PRNP_+_-_(57g)	0.14748	0.00000	0.00000
514	PRNP	PRNP_extended_+_-_(57g)	0.14748	0.00000	0.00000
667	SP1	SP1_+_-_(127g)	0.14684	0.00000	0.00000
810	ZNF281	ZNF281_extended_+_-_(59g)	0.14412	0.00000	0.00000
809	ZNF281	ZNF281_+_-_(59g)	0.14412	0.00000	0.00000
261	IRF1	IRF1_+_+_(13g)	0.14364	0.00000	0.00000
262	IRF1	IRF1_extended_+_+_(13g)	0.14364	0.00000	0.00000
258	IKZF2	IKZF2_extended_+_+_(20g)	0.13538	0.00000	0.00000
257	IKZF2	IKZF2_+_+_(20g)	0.13538	0.00000	0.00000
501	POU3F3	POU3F3_extended_+_+_(16g)	0.13220	0.00000	0.00000
309	KLF3	KLF3_extended_+_+_(87g)	0.13051	0.00000	0.00000
308	KLF3	KLF3_+_+_(86g)	0.12991	0.00000	0.00000
749	TFAP4	TFAP4_extended_+_+_(12g)	0.12834	0.00000	0.00000
748	TFAP4	TFAP4_+_+_(12g)	0.12834	0.00000	0.00000
500	POU3F3	POU3F3_+_+_(13g)	0.12520	0.00000	0.00000
191	FOXK1	FOXK1_extended_+_+_(56g)	0.12050	0.00000	0.00000
504	POU3F4	POU3F4_extended_+_-_(19g)	0.11885	0.00000	0.00000
763	TRIM28	TRIM28_extended_+_-_(43g)	0.11703	0.00000	0.00000
762	TRIM28	TRIM28_+_-_(43g)	0.11703	0.00000	0.00000
669	SP1	SP1_extended_+_-_(134g)	0.11635	0.00000	0.00000
134	ELF4	ELF4_extended_+_-_(34g)	0.11271	0.00001	0.00001
802	ZNF134	ZNF134_extended_+_-_(24g)	0.10510	0.00003	0.00003
800	ZNF134	ZNF134_+_-_(24g)	0.10510	0.00003	0.00003
776	YY1	YY1_extended_+_-_(89g)	0.10488	0.00003	0.00003
774	YY1	YY1_+_-_(92g)	0.10408	0.00003	0.00004
284	JUND	JUND_extended_+_-_(69g)	0.10234	0.00004	0.00005
358	MEF2B	MEF2B_+_+_(32g)	0.09835	0.00008	0.00010
688	STAT1	STAT1_+_-_(16g)	0.09013	0.00031	0.00036
690	STAT1	STAT1_extended_+_-_(16g)	0.09013	0.00031	0.00036
193	FOXK2	FOXK2_extended_+_-_(146g)	0.08913	0.00036	0.00042
442	NR2C2	NR2C2_+_+_(77g)	0.08909	0.00036	0.00042
444	NR2C2	NR2C2_extended_+_+_(77g)	0.08909	0.00036	0.00042
315	KLF7	KLF7_+_+_(10g)	0.08863	0.00039	0.00045
102	E2F4	E2F4_extended_+_+_(56g)	0.08683	0.00051	0.00059
139	ELK4	ELK4_+_+_(194g)	0.08477	0.00069	0.00079
140	ELK4	ELK4_extended_+_+_(194g)	0.08477	0.00069	0.00079
699	STAT5A	STAT5A_extended_+_+_(9g)	0.08343	0.00084	0.00095
697	STAT5A	STAT5A_+_+_(9g)	0.08343	0.00084	0.00095
700	STAT5A	STAT5A_extended_+_-_(12g)	0.08339	0.00084	0.00096
698	STAT5A	STAT5A_+_-_(12g)	0.08339	0.00084	0.00096
132	ELF4	ELF4_+_-_(20g)	0.07817	0.00175	0.00199
603	SOX12	SOX12_+_-_(21g)	0.07762	0.00189	0.00213
821	ZNF444	ZNF444_+_-_(11g)	0.07647	0.00221	0.00248
822	ZNF444	ZNF444_extended_+_-_(11g)	0.07647	0.00221	0.00248
367	MEF2D	MEF2D_extended_+_-_(20g)	0.07553	0.00250	0.00281
268	IRF3	IRF3_extended_+_+_(22g)	0.07538	0.00255	0.00286
281	JUND	JUND_+_-_(38g)	0.07013	0.00501	0.00558
0	ACO1	ACO1_+_+_(23g)	0.06572	0.00855	0.00948
2	ACO1	ACO1_extended_+_+_(23g)	0.06572	0.00855	0.00948
686	SRF	SRF_extended_+_-_(14g)	0.06265	0.01220	0.01351
115	EHF	EHF_extended_+_+_(16g)	0.06080	0.01499	0.01657
113	EHF	EHF_+_+_(16g)	0.06080	0.01499	0.01657
219	GABPB1	GABPB1_extended_+_+_(99g)	0.05986	0.01664	0.01834
217	GABPB1	GABPB1_+_+_(99g)	0.05986	0.01664	0.01834
103	E2F4	E2F4_extended_+_-_(41g)	0.05951	0.01729	0.01903
703	TAF1	TAF1_+_-_(56g)	0.05855	0.01918	0.02105
704	TAF1	TAF1_extended_+_-_(56g)	0.05855	0.01918	0.02105
267	IRF3	IRF3_+_+_(10g)	0.05823	0.01984	0.02175
116	EHF	EHF_extended_+_-_(13g)	0.05788	0.02060	0.02252
114	EHF	EHF_+_-_(13g)	0.05788	0.02060	0.02252
101	E2F4	E2F4_+_-_(15g)	0.05465	0.02881	0.03126
303	KLF16	KLF16_+_-_(12g)	0.03726	0.13627	0.14573
305	KLF16	KLF16_extended_+_-_(13g)	0.03648	0.14472	0.15457
341	MAFG	MAFG_+_-_(27g)	0.03643	0.14522	0.15471
342	MAFG	MAFG_extended_+_-_(27g)	0.03643	0.14522	0.15471
463	PAX5	PAX5_+_+_(16g)	0.02960	0.23668	0.24989
464	PAX5	PAX5_extended_+_+_(16g)	0.02960	0.23668	0.24989
511	PRNP	PRNP_+_+_(10g)	0.02942	0.23958	0.25231
252	HSF2	HSF2_extended_+_+_(10g)	0.02809	0.26142	0.27497
780	ZBTB14	ZBTB14_extended_+_-_(21g)	0.02721	0.27679	0.29040
779	ZBTB14	ZBTB14_+_-_(21g)	0.02721	0.27679	0.29040
271	IRF9	IRF9_+_+_(21g)	0.02512	0.31529	0.32872
248	HOMEZ	HOMEZ_extended_+_-_(16g)	0.01939	0.43834	0.45080
247	HOMEZ	HOMEZ_+_-_(16g)	0.01939	0.43834	0.45080
724	TCF3	TCF3_extended_+_-_(60g)	0.01825	0.46560	0.47824
711	TBP	TBP_+_+_(66g)	0.01581	0.52751	0.53917
712	TBP	TBP_extended_+_+_(66g)	0.01581	0.52751	0.53917
445	NR2C2	NR2C2_extended_+_-_(69g)	0.01068	0.66941	0.68253
443	NR2C2	NR2C2_+_-_(69g)	0.01068	0.66941	0.68253
799	ZNF134	ZNF134_+_+_(65g)	0.00791	0.75201	0.76302
801	ZNF134	ZNF134_extended_+_+_(65g)	0.00791	0.75201	0.76302
221	GPANK1	GPANK1_+_+_(13g)	0.00646	0.79630	0.80501
223	GPANK1	GPANK1_extended_+_+_(13g)	0.00646	0.79630	0.80501
582	RXRB	RXRB_extended_+_+_(18g)	0.00418	0.86737	0.87473
580	RXRB	RXRB_+_+_(18g)	0.00418	0.86737	0.87473
722	TCF3	TCF3_+_-_(51g)	0.00333	0.89415	0.90065
723	TCF3	TCF3_extended_+_+_(43g)	0.00238	0.92432	0.92990
229	HDAC1	HDAC1_+_-_(19g)	0.00018	0.99425	0.99425
230	HDAC1	HDAC1_extended_+_-_(19g)	0.00018	0.99425	0.99425
803	ZNF143	ZNF143_+_+_(66g)	-0.00038	0.98787	0.99025
804	ZNF143	ZNF143_extended_+_+_(66g)	-0.00038	0.98787	0.99025
721	TCF3	TCF3_+_+_(40g)	-0.00064	0.97974	0.98448
272	IRF9	IRF9_extended_+_+_(26g)	-0.00712	0.77607	0.78647
540	RELA	RELA_extended_+_-_(18g)	-0.00847	0.73495	0.74753
752	THRA	THRA_extended_+_+_(79g)	-0.00944	0.70591	0.71887
436	NKX3-1	NKX3-1_+_+_(12g)	-0.01702	0.49628	0.50851
437	NKX3-1	NKX3-1_extended_+_+_(12g)	-0.01702	0.49628	0.50851
529	RARA	RARA_+_+_(16g)	-0.02082	0.40518	0.41773
530	RARA	RARA_extended_+_+_(16g)	-0.02082	0.40518	0.41773
67	CPSF4	CPSF4_+_-_(11g)	-0.02288	0.36045	0.37254
69	CPSF4	CPSF4_extended_+_-_(11g)	-0.02288	0.36045	0.37254
385	MZF1	MZF1_extended_+_+_(78g)	-0.02348	0.34786	0.36042
384	MZF1	MZF1_+_+_(78g)	-0.02348	0.34786	0.36042
404	NFATC3	NFATC3_extended_+_+_(33g)	-0.02367	0.34413	0.35745
402	NFATC3	NFATC3_+_+_(33g)	-0.02367	0.34413	0.35745
76	CTCF	CTCF_extended_+_+_(160g)	-0.02436	0.33010	0.34373
820	ZNF3	ZNF3_extended_+_-_(16g)	-0.02587	0.30113	0.31436
818	ZNF3	ZNF3_+_-_(16g)	-0.02587	0.30113	0.31436
346	MAGEF1	MAGEF1_extended_+_-_(16g)	-0.02649	0.28971	0.30319
345	MAGEF1	MAGEF1_+_-_(16g)	-0.02649	0.28971	0.30319
564	RFX5	RFX5_extended_+_+_(17g)	-0.02945	0.23913	0.25217
731	TCF4	TCF4_extended_-_+_(14g)	-0.02960	0.23665	0.24989
569	RFXAP	RFXAP_extended_+_-_(35g)	-0.03031	0.22563	0.23914
568	RFXAP	RFXAP_+_-_(35g)	-0.03031	0.22563	0.23914
727	TCF4	TCF4_-_+_(13g)	-0.03125	0.21156	0.22479
563	RFX5	RFX5_+_+_(51g)	-0.03477	0.16443	0.17494
204	FOXO3	FOXO3_+_-_(149g)	-0.04070	0.10363	0.11096
604	SOX12	SOX12_extended_+_-_(79g)	-0.04169	0.09551	0.10240
206	FOXO3	FOXO3_extended_+_-_(150g)	-0.04221	0.09148	0.09820
811	ZNF341	ZNF341_+_+_(36g)	-0.04801	0.05484	0.05895
813	ZNF341	ZNF341_extended_+_+_(36g)	-0.04801	0.05484	0.05895
27	ATF1	ATF1_+_+_(21g)	-0.04975	0.04662	0.05024
45	BCLAF1	BCLAF1_extended_+_+_(115g)	-0.05317	0.03344	0.03608
43	BCLAF1	BCLAF1_+_+_(115g)	-0.05317	0.03344	0.03608
570	RIOK2	RIOK2_+_+_(20g)	-0.05405	0.03061	0.03312
572	RIOK2	RIOK2_extended_+_+_(20g)	-0.05405	0.03061	0.03312
192	FOXK2	FOXK2_extended_+_+_(45g)	-0.05484	0.02827	0.03070
706	TAF7	TAF7_+_-_(40g)	-0.05600	0.02508	0.02728
708	TAF7	TAF7_extended_+_-_(40g)	-0.05600	0.02508	0.02728
354	MECP2	MECP2_extended_+_+_(21g)	-0.05748	0.02150	0.02344
353	MECP2	MECP2_+_+_(21g)	-0.05748	0.02150	0.02344
265	IRF2	IRF2_extended_+_+_(96g)	-0.06838	0.00621	0.00691
218	GABPB1	GABPB1_+_-_(17g)	-0.07239	0.00377	0.00420
220	GABPB1	GABPB1_extended_+_-_(17g)	-0.07239	0.00377	0.00420
675	SP4	SP4_extended_+_-_(124g)	-0.07400	0.00306	0.00342
673	SP3	SP3_extended_+_+_(349g)	-0.07445	0.00289	0.00323
215	GABPA	GABPA_extended_+_+_(105g)	-0.07684	0.00210	0.00237
214	GABPA	GABPA_+_+_(101g)	-0.07793	0.00181	0.00205
474	PBX4	PBX4_extended_+_-_(11g)	-0.08178	0.00106	0.00120
136	ELK1	ELK1_extended_+_+_(70g)	-0.08267	0.00093	0.00106
135	ELK1	ELK1_+_+_(70g)	-0.08267	0.00093	0.00106
320	KLF8	KLF8_extended_+_+_(11g)	-0.08402	0.00077	0.00088
830	ZNF579	ZNF579_extended_+_+_(20g)	-0.08411	0.00076	0.00087
829	ZNF579	ZNF579_+_+_(20g)	-0.08411	0.00076	0.00087
674	SP4	SP4_+_-_(129g)	-0.08612	0.00056	0.00065
476	PHF8	PHF8_extended_+_+_(29g)	-0.08631	0.00055	0.00063
475	PHF8	PHF8_+_+_(29g)	-0.08631	0.00055	0.00063
319	KLF8	KLF8_+_+_(10g)	-0.08891	0.00037	0.00043
189	FOXJ2	FOXJ2_+_+_(22g)	-0.09763	0.00009	0.00011
672	SP3	SP3_+_+_(335g)	-0.09796	0.00009	0.00010
190	FOXJ2	FOXJ2_extended_+_+_(24g)	-0.09833	0.00008	0.00010
539	RELA	RELA_extended_+_+_(14g)	-0.09937	0.00007	0.00008
263	IRF2	IRF2_+_+_(75g)	-0.10292	0.00004	0.00004
216	GABPA	GABPA_extended_+_-_(133g)	-0.10680	0.00002	0.00002
692	STAT2	STAT2_extended_+_+_(22g)	-0.10943	0.00001	0.00001
691	STAT2	STAT2_+_+_(22g)	-0.10943	0.00001	0.00001
816	ZNF354A	ZNF354A_extended_+_-_(10g)	-0.11273	0.00001	0.00001
815	ZNF354A	ZNF354A_+_-_(10g)	-0.11273	0.00001	0.00001
213	FOXP4	FOXP4_extended_+_-_(14g)	-0.11343	0.00001	0.00001
671	SP2	SP2_extended_+_+_(120g)	-0.11364	0.00001	0.00001
670	SP2	SP2_+_+_(81g)	-0.12044	0.00000	0.00000
619	SOX21	SOX21_extended_-_+_(4g)	-0.13010	0.00000	0.00000
235	HDX	HDX_+_+_(52g)	-0.13094	0.00000	0.00000
236	HDX	HDX_extended_+_+_(52g)	-0.13094	0.00000	0.00000
796	ZEB2	ZEB2_-_+_(11g)	-0.14769	0.00000	0.00000
798	ZEB2	ZEB2_extended_-_+_(11g)	-0.14769	0.00000	0.00000
246	HNF4G	HNF4G_extended_-_+_(7g)	-0.14786	0.00000	0.00000
244	HNF4G	HNF4G_-_+_(7g)	-0.14786	0.00000	0.00000
74	CTCF	CTCF_+_+_(125g)	-0.15006	0.00000	0.00000
68	CPSF4	CPSF4_extended_+_+_(24g)	-0.15028	0.00000	0.00000
85	DBP	DBP_+_+_(10g)	-0.15453	0.00000	0.00000
573	RIOK2	RIOK2_extended_+_-_(22g)	-0.16081	0.00000	0.00000
571	RIOK2	RIOK2_+_-_(22g)	-0.16081	0.00000	0.00000
807	ZNF263	ZNF263_+_-_(14g)	-0.17445	0.00000	0.00000
808	ZNF263	ZNF263_extended_+_-_(14g)	-0.17445	0.00000	0.00000
612	SOX15	SOX15_extended_+_+_(10g)	-0.18080	0.00000	0.00000
609	SOX15	SOX15_+_+_(10g)	-0.18080	0.00000	0.00000
286	JUND	JUND_extended_-_-_(17g)	-0.18124	0.00000	0.00000
283	JUND	JUND_-_-_(17g)	-0.18124	0.00000	0.00000
285	JUND	JUND_extended_-_+_(11g)	-0.19399	0.00000	0.00000
282	JUND	JUND_-_+_(9g)	-0.19504	0.00000	0.00000
481	PKM	PKM_extended_+_+_(24g)	-0.20123	0.00000	0.00000
479	PKM	PKM_+_+_(24g)	-0.20123	0.00000	0.00000
28	ATF1	ATF1_extended_+_+_(28g)	-0.20515	0.00000	0.00000
71	CREB1	CREB1_extended_+_+_(40g)	-0.21076	0.00000	0.00000
70	CREB1	CREB1_+_+_(40g)	-0.21076	0.00000	0.00000
7	AGGF1	AGGF1_+_-_(33g)	-0.21304	0.00000	0.00000
9	AGGF1	AGGF1_extended_+_-_(33g)	-0.21304	0.00000	0.00000
433	NFYC	NFYC_+_-_(15g)	-0.21636	0.00000	0.00000
483	PKNOX1	PKNOX1_extended_+_-_(11g)	-0.21916	0.00000	0.00000
205	FOXO3	FOXO3_extended_+_+_(52g)	-0.22874	0.00000	0.00000
17	ARNT	ARNT_extended_+_-_(10g)	-0.22931	0.00000	0.00000
16	ARNT	ARNT_+_-_(10g)	-0.22931	0.00000	0.00000
203	FOXO3	FOXO3_+_+_(51g)	-0.23182	0.00000	0.00000
23	ASCL1	ASCL1_-_+_(10g)	-0.23300	0.00000	0.00000
65	CHURC1	CHURC1_extended_+_-_(10g)	-0.24091	0.00000	0.00000
63	CHURC1	CHURC1_+_-_(10g)	-0.24091	0.00000	0.00000
477	PICK1	PICK1_+_+_(23g)	-0.24619	0.00000	0.00000
478	PICK1	PICK1_extended_+_+_(23g)	-0.24619	0.00000	0.00000
288	JUN	JUN_-_-_(5g)	-0.25056	0.00000	0.00000
290	JUN	JUN_extended_-_-_(5g)	-0.25056	0.00000	0.00000
614	SOX15	SOX15_extended_-_+_(10g)	-0.25354	0.00000	0.00000
611	SOX15	SOX15_-_+_(10g)	-0.25354	0.00000	0.00000
370	MEIS2	MEIS2_extended_-_+_(7g)	-0.25401	0.00000	0.00000
814	ZNF341	ZNF341_extended_+_-_(10g)	-0.26374	0.00000	0.00000
812	ZNF341	ZNF341_+_-_(10g)	-0.26374	0.00000	0.00000
434	NFYC	NFYC_extended_+_-_(15g)	-0.27620	0.00000	0.00000
633	SOX4	SOX4_-_+_(19g)	-0.28562	0.00000	0.00000
259	ING4	ING4_+_+_(12g)	-0.29175	0.00000	0.00000
260	ING4	ING4_extended_+_+_(12g)	-0.29175	0.00000	0.00000
378	MTF1	MTF1_extended_+_+_(51g)	-0.29353	0.00000	0.00000
226	GTF2I	GTF2I_extended_+_+_(25g)	-0.30054	0.00000	0.00000
225	GTF2I	GTF2I_+_+_(25g)	-0.30054	0.00000	0.00000
636	SOX4	SOX4_extended_-_+_(21g)	-0.30060	0.00000	0.00000
129	ELF2	ELF2_extended_-_+_(29g)	-0.31150	0.00000	0.00000
126	ELF2	ELF2_-_+_(27g)	-0.31171	0.00000	0.00000
766	VEZF1	VEZF1_-_+_(24g)	-0.32206	0.00000	0.00000
768	VEZF1	VEZF1_extended_-_+_(24g)	-0.32206	0.00000	0.00000
119	ELF1	ELF1_-_+_(23g)	-0.32516	0.00000	0.00000
123	ELF1	ELF1_extended_-_+_(23g)	-0.32516	0.00000	0.00000
26	ASCL1	ASCL1_extended_-_+_(16g)	-0.32663	0.00000	0.00000
732	TCF4	TCF4_extended_-_-_(12g)	-0.32747	0.00000	0.00000
728	TCF4	TCF4_-_-_(9g)	-0.32967	0.00000	0.00000
127	ELF2	ELF2_-_-_(11g)	-0.34495	0.00000	0.00000
195	FOXN2	FOXN2_extended_-_+_(14g)	-0.35486	0.00000	0.00000
130	ELF2	ELF2_extended_-_-_(14g)	-0.35579	0.00000	0.00000
505	POU6F1	POU6F1_extended_+_+_(41g)	-0.35826	0.00000	0.00000
556	RFX3	RFX3_extended_-_-_(7g)	-0.37059	0.00000	0.00000
554	RFX3	RFX3_-_-_(7g)	-0.37059	0.00000	0.00000
750	THRA	THRA_+_+_(44g)	-0.37346	0.00000	0.00000
591	SMAD1	SMAD1_extended_-_+_(10g)	-0.38317	0.00000	0.00000
590	SMAD1	SMAD1_-_+_(10g)	-0.38317	0.00000	0.00000
84	CUX1	CUX1_extended_-_-_(10g)	-0.38487	0.00000	0.00000
81	CUX1	CUX1_-_-_(10g)	-0.38487	0.00000	0.00000
665	SOX9	SOX9_extended_-_-_(11g)	-0.38496	0.00000	0.00000
662	SOX9	SOX9_-_-_(11g)	-0.38496	0.00000	0.00000
737	TCF7L2	TCF7L2_-_-_(11g)	-0.40316	0.00000	0.00000
621	SOX2	SOX2_-_+_(16g)	-0.41185	0.00000	0.00000
625	SOX2	SOX2_extended_-_+_(16g)	-0.41185	0.00000	0.00000
550	RFX2	RFX2_-_-_(17g)	-0.42644	0.00000	0.00000
552	RFX2	RFX2_extended_-_-_(17g)	-0.42644	0.00000	0.00000
276	JDP2	JDP2_extended_-_-_(11g)	-0.44133	0.00000	0.00000
274	JDP2	JDP2_-_-_(11g)	-0.44133	0.00000	0.00000
559	RFX4	RFX4_-_-_(44g)	-0.44747	0.00000	0.00000
78	CTCF	CTCF_extended_-_-_(7g)	-0.45121	0.00000	0.00000
649	SOX6	SOX6_extended_-_+_(157g)	-0.45203	0.00000	0.00000
645	SOX6	SOX6_-_+_(144g)	-0.45409	0.00000	0.00000
646	SOX6	SOX6_-_-_(8g)	-0.45909	0.00000	0.00000
401	NFAT5	NFAT5_extended_-_-_(15g)	-0.46115	0.00000	0.00000
398	NFAT5	NFAT5_-_-_(15g)	-0.46115	0.00000	0.00000
494	POU2F1	POU2F1_extended_-_-_(11g)	-0.46872	0.00000	0.00000
524	PURA	PURA_-_-_(7g)	-0.46881	0.00000	0.00000
526	PURA	PURA_extended_-_-_(7g)	-0.46881	0.00000	0.00000
562	RFX4	RFX4_extended_-_-_(44g)	-0.47094	0.00000	0.00000
426	NFIC	NFIC_extended_-_-_(15g)	-0.48520	0.00000	0.00000
424	NFIC	NFIC_-_-_(15g)	-0.48520	0.00000	0.00000
146	EMX2	EMX2_extended_-_+_(10g)	-0.48590	0.00000	0.00000
622	SOX2	SOX2_-_-_(29g)	-0.48735	0.00000	0.00000
659	SOX8	SOX8_extended_-_-_(43g)	-0.49344	0.00000	0.00000
716	TCF12	TCF12_-_-_(159g)	-0.49352	0.00000	0.00000
656	SOX8	SOX8_-_-_(40g)	-0.49474	0.00000	0.00000
720	TCF12	TCF12_extended_-_-_(162g)	-0.49511	0.00000	0.00000
626	SOX2	SOX2_extended_-_-_(31g)	-0.49539	0.00000	0.00000
468	PAX6	PAX6_extended_-_-_(7g)	-0.50860	0.00000	0.00000
784	ZBTB20	ZBTB20_-_-_(12g)	-0.53503	0.00000	0.00000
786	ZBTB20	ZBTB20_extended_-_-_(12g)	-0.53503	0.00000	0.00000
520	PRRX1	PRRX1_extended_-_-_(50g)	-0.53665	0.00000	0.00000
517	PRRX1	PRRX1_-_-_(47g)	-0.54149	0.00000	0.00000
356	MEF2A	MEF2A_-_-_(13g)	-0.54730	0.00000	0.00000
188	FOXG1	FOXG1_extended_-_+_(25g)	-0.54948	0.00000	0.00000
383	MYEF2	MYEF2_extended_-_-_(8g)	-0.55894	0.00000	0.00000
34	BACH1	BACH1_-_+_(11g)	-0.55989	0.00000	0.00000
40	BACH2	BACH2_extended_-_-_(13g)	-0.58589	0.00000	0.00000
38	BACH2	BACH2_-_-_(12g)	-0.58946	0.00000	0.00000
412	NFE2L2	NFE2L2_extended_-_+_(21g)	-0.60579	0.00000	0.00000
410	NFE2L2	NFE2L2_-_+_(21g)	-0.60579	0.00000	0.00000
120	ELF1	ELF1_-_-_(33g)	-0.60647	0.00000	0.00000
124	ELF1	ELF1_extended_-_-_(33g)	-0.60647	0.00000	0.00000
36	BACH1	BACH1_extended_-_+_(13g)	-0.60754	0.00000	0.00000
719	TCF12	TCF12_extended_-_+_(235g)	-0.61310	0.00000	0.00000
548	RFX1	RFX1_extended_-_-_(7g)	-0.61311	0.00000	0.00000
546	RFX1	RFX1_-_-_(6g)	-0.61920	0.00000	0.00000
184	FOS	FOS_extended_-_+_(52g)	-0.62457	0.00000	0.00000
182	FOS	FOS_-_+_(52g)	-0.62457	0.00000	0.00000
280	JUNB	JUNB_extended_-_+_(16g)	-0.63040	0.00000	0.00000
278	JUNB	JUNB_-_+_(16g)	-0.63040	0.00000	0.00000
212	FOXP2	FOXP2_extended_-_+_(10g)	-0.65009	0.00000	0.00000
561	RFX4	RFX4_extended_-_+_(54g)	-0.65041	0.00000	0.00000
202	FOXO1	FOXO1_extended_-_-_(47g)	-0.65295	0.00000	0.00000
199	FOXO1	FOXO1_-_-_(47g)	-0.65295	0.00000	0.00000
642	SOX5	SOX5_extended_-_+_(249g)	-0.65540	0.00000	0.00000
715	TCF12	TCF12_-_+_(161g)	-0.65618	0.00000	0.00000
639	SOX5	SOX5_-_+_(225g)	-0.65626	0.00000	0.00000
558	RFX4	RFX4_-_+_(49g)	-0.65850	0.00000	0.00000
210	FOXP2	FOXP2_-_+_(9g)	-0.65910	0.00000	0.00000
371	MEIS2	MEIS2_extended_-_-_(15g)	-0.67686	0.00000	0.00000
343	MAF	MAF_-_-_(14g)	-0.67815	0.00000	0.00000
344	MAF	MAF_extended_-_-_(12g)	-0.68341	0.00000	0.00000
173	FLI1	FLI1_-_-_(9g)	-0.68762	0.00000	0.00000
432	NFIX	NFIX_extended_-_-_(12g)	-0.68981	0.00000	0.00000
429	NFIX	NFIX_-_-_(12g)	-0.68981	0.00000	0.00000
294	KLF12	KLF12_-_+_(57g)	-0.72597	0.00000	0.00000
298	KLF12	KLF12_extended_-_+_(57g)	-0.72597	0.00000	0.00000
364	MEF2C	MEF2C_extended_-_-_(106g)	-0.73176	0.00000	0.00000
361	MEF2C	MEF2C_-_-_(112g)	-0.73376	0.00000	0.00000
295	KLF12	KLF12_-_-_(16g)	-0.74768	0.00000	0.00000
299	KLF12	KLF12_extended_-_-_(16g)	-0.74768	0.00000	0.00000
420	NFIA	NFIA_extended_-_-_(71g)	-0.76011	0.00000	0.00000
416	NFIA	NFIA_-_-_(71g)	-0.76011	0.00000	0.00000
419	NFIA	NFIA_extended_-_+_(72g)	-0.78629	0.00000	0.00000
415	NFIA	NFIA_-_+_(72g)	-0.78629	0.00000	0.00000

We can additionally plot eRegulon enrichment versus TF expression for each pseudobulk.

[11]:

# Region based
%matplotlib inline
import seaborn as sns
sns.set_style("white")
colors = ["#E9842C","#F8766D", "#BC9D00", "#00C0B4", "#9CA700", "#6FB000", "#00B813", "#00BD61", "#00C08E", "#00BDD4",
           "#00A7FF", "#7F96FF", "#E26EF7", "#FF62BF", "#D69100", "#BC81FF"]
categories = sorted(set(scplus_obj.metadata_cell['ACC_Seurat_cell_type']))
color_dict = dict(zip(categories, colors[0:len(categories)]))
prune_plot(scplus_obj,
           'SOX5_-_+',
           pseudobulk_variable = 'ACC_Seurat_cell_type',
           show_dot_plot = True,
           show_line_plot = False,
           color_dict = color_dict,
           use_pseudobulk = True,
           auc_key = 'eRegulon_AUC',
           signature_key = 'Region_based',
           seed=555)

[12]:

# Gene based
%matplotlib inline
sns.set_style("white")
colors = ["#E9842C","#F8766D", "#BC9D00", "#00C0B4", "#9CA700", "#6FB000", "#00B813", "#00BD61", "#00C08E", "#00BDD4",
           "#00A7FF", "#7F96FF", "#E26EF7", "#FF62BF", "#D69100", "#BC81FF"]
categories = sorted(set(scplus_obj.metadata_cell['ACC_Seurat_cell_type']))
color_dict = dict(zip(categories, colors[0:len(categories)]))
prune_plot(scplus_obj,
           'SOX5_-_+',
           pseudobulk_variable = 'ACC_Seurat_cell_type',
           show_dot_plot = True,
           show_line_plot = False,
           color_dict = color_dict,
           use_pseudobulk = True,
           auc_key = 'eRegulon_AUC',
           signature_key = 'Gene_based',
           seed=555)

We will select a subset of cistromes with good correlation with the TF. We will only use the extended eRegulon if there is not a direct eRegulon available.

[3]:

# Direct cistromes
df_corr = scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_eGRN_gene_based']
selected_cistromes_1 = df_corr[(~df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] > 0.5)]
selected_cistromes_2 = df_corr[(~df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] < -0.5)]
df_corr = scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_eGRN_region_based']
selected_cistromes_3 = df_corr[(~df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] > 0.5)]
selected_cistromes_4 = df_corr[(~df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] < -0.5)]
selected_cistromes_direct = pd.concat([selected_cistromes_1, selected_cistromes_2, selected_cistromes_3, selected_cistromes_4])
# Extended cistromes
df_corr = scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_eGRN_gene_based']
selected_cistromes_1 = df_corr[(df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] > 0.6)]
selected_cistromes_2 = df_corr[(df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] < -0.6)]
df_corr = scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_eGRN_region_based']
selected_cistromes_3 = df_corr[(df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] > 0.6)]
selected_cistromes_4 = df_corr[(df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] < -0.6)]
selected_cistromes_extended = pd.concat([selected_cistromes_1, selected_cistromes_2, selected_cistromes_3, selected_cistromes_4])
# Only use extended if direct not available
selected_cistromes_extended = selected_cistromes_extended[~selected_cistromes_extended['TF'].isin(selected_cistromes_direct['TF'])]
# Combine
selected_cistromes = pd.concat([selected_cistromes_direct, selected_cistromes_extended]).drop_duplicates()

To assess the relationship between regions and genes in the same eRegulon, we can generate a dotplot per cell type.

[34]:

## Format data
from scenicplus.plotting.dotplot import *
# Gene based
dotplot_df_gene = generate_dotplot_df_AUC(scplus_obj,
                       'eRegulon_AUC',
                       'Gene_based',
                       'ACC_Seurat_cell_type',
                       subset = None,
                       subset_cistromes = selected_cistromes['Cistrome'],
                       use_pseudobulk = False,
                       normalize_expression = False,
                       standardize_expression = True,
                       standardize_auc = True)
dotplot_df_gene['is_extended'] = ['_extended' in x for x in dotplot_df_gene['Name']]
dotplot_df_gene['Consensus_name'] = [re.sub('_\(.*\)', '', cistrome_name) for cistrome_name in dotplot_df_gene['Name']]
# Region based
dotplot_df_region = generate_dotplot_df_AUC(scplus_obj,
                       'eRegulon_AUC',
                       'Region_based',
                       'ACC_Seurat_cell_type',
                       subset = None,
                       subset_cistromes = selected_cistromes['Cistrome'],
                       use_pseudobulk = False,
                       normalize_expression = False,
                       standardize_expression = True,
                       standardize_auc = True)
dotplot_df_region['is_extended'] = ['_extended' in x for x in dotplot_df_region['Name']]
dotplot_df_region['Consensus_name'] = [re.sub('_\(.*\)', '', cistrome_name) for cistrome_name in dotplot_df_region['Name']]
# Merge
dotplot_df = pd.merge(dotplot_df_gene, dotplot_df_region, on=['Group', 'Consensus_name'])
dotplot_df.columns = ['TF', 'Group', 'TF_expression_gene', 'Name_gene', 'eRegulon_AUC_gene', 'is_extended_gene', 'Consensus_name', 'TF_region', 'TF_expression_region', 'Name_region', 'eRegulon_AUC_region', 'is_extended_gene']

[35]:

dotplot(dotplot_df,
                     ax = None,
                     region_set_key = 'Name_gene',
                     order_cistromes_by_max = 'eRegulon_AUC_gene',
                     cluster = 'group', #can be group, TF or both
                     color_var = 'eRegulon_AUC_region',
                     size_var = 'eRegulon_AUC_gene',
                     order_group = None,
                     order_cistromes = None,
                     n_clust = 10,
                     min_point_size = 1,
                     max_point_size = 15,
                     cmap = 'viridis',
                     vmin = 0,
                     vmax = 1,
                     x_tick_rotation = 45,
                     x_tick_ha = 'right',
                     fontsize  = 9,
                     z_score_expr = False,
                     z_score_enr = False,
                     grid_color = 'grey',
                     grid_lw = 0.5,
                     highlight = None,
                     highlight_lw = 1,
                     plotly_height=1500,
                     use_plotly = True)

In addition, to assess which eRegulons tend to be enriched in the same group of cells we can generate a correlation plot as well.

[36]:

#from scenicplus.plotting.correlation_plot import *
correlation_heatmap(scplus_obj,
                    auc_key = 'eRegulon_AUC',
                    signature_keys = ['Gene_based'],
                    selected_regulons = selected_cistromes['Cistrome'],
                    fcluster_threshold = 0.1,
                    fontsize = 3)

We can also check the overlap between eRegulons:

[46]:

from scenicplus.plotting.correlation_plot import *
jaccard_heatmap(scplus_obj,
                    gene_or_region_based = 'Gene_based',
                    signature_key = 'eRegulon_signatures',
                    selected_regulons = selected_cistromes['Cistrome'].tolist(),
                    fcluster_threshold = 0.1,
                    fontsize = 3)

We can also binarize the eRegulons as in SCENIC. This information will be used afterwards for generating the loom file.

[21]:

binarize_AUC(scplus_obj,
             auc_key='eRegulon_AUC',
             out_key='eRegulon_AUC_thresholds',
             signature_keys=['Gene_based', 'Region_based'],
             n_cpu=8)

[22]:

import dill
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  dill.dump(scplus_obj, f)

C. eGRN dimensionality reduction

We can also use the eRegulons for clustering and visualization. The combination of both gene and region based eRegulons results in a better clustering.

[1]:

import dill
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = dill.load(infile)
infile.close()

[23]:

from scenicplus.dimensionality_reduction import *
run_eRegulons_umap(scplus_obj,
                   scale=True, signature_keys=['Gene_based', 'Region_based'])
run_eRegulons_tsne(scplus_obj,
                   scale=True, signature_keys=['Gene_based', 'Region_based'])

We can compare with the previous annotations based on the analyses of the scRNA-seq layer with VSN (Scanpy) and Seurat.

[24]:

plot_metadata(scplus_obj,
                 reduction_name='eRegulons_UMAP',
                 variables=['ACC_VSN_cell_type', 'ACC_Seurat_cell_type'],
                 num_columns=2,
                 text_size=10,
                 dot_size=5)

_images/single_sample_tutorial_116_0.png

We can also compare with using the layers independently as well.

[25]:

run_eRegulons_umap(scplus_obj,
                   scale=True, signature_keys=['Gene_based'],
                   reduction_name='eRegulons_UMAP_gb')
run_eRegulons_tsne(scplus_obj,
                   scale=True, signature_keys=['Gene_based'],
                   reduction_name='eRegulons_tSNE_gb')
run_eRegulons_umap(scplus_obj,
                   scale=True, signature_keys=['Region_based'],
                   reduction_name='eRegulons_UMAP_rb')
run_eRegulons_tsne(scplus_obj,
                   scale=True, signature_keys=['Region_based'],
                   reduction_name='eRegulons_tSNE_rb')

[26]:

plot_metadata(scplus_obj,
                 reduction_name='eRegulons_UMAP_gb',
                 variables=['ACC_VSN_cell_type', 'ACC_Seurat_cell_type'],
                 num_columns=2,
                 text_size=10,
                 dot_size=5)

_images/single_sample_tutorial_119_0.png

[27]:

plot_metadata(scplus_obj,
                 reduction_name='eRegulons_UMAP_rb',
                 variables=['ACC_VSN_cell_type', 'ACC_Seurat_cell_type'],
                 num_columns=2,
                 text_size=10,
                 dot_size=5)

_images/single_sample_tutorial_120_0.png

In addition, we can also use the eRegulon enrichment for clustering.

[30]:

find_clusters(scplus_obj,
              signature_keys=['Gene_based', 'Region_based'],
              k = 10,
              res = [0.6, 1.2, 1.5],
              prefix = 'SCENIC+_',
              scale = True)

[31]:

plot_metadata(scplus_obj,
                 reduction_name='eRegulons_UMAP',
                 variables=['ACC_VSN_cell_type', 'SCENIC+_leiden_10_1.5'],
                 num_columns=2,
                 text_size=10,
                 dot_size=5)

_images/single_sample_tutorial_123_0.png

We can also plot TF expression and eRegulon enrichment (based on target genes and regions, respectively).

[35]:

# For example, for some OL TFs
plot_eRegulon(scplus_obj,
              reduction_name='eRegulons_UMAP',
              selected_regulons=['PRRX2_+_+','OLIG2_+_+', 'SOX10_+_+', 'TCF12_+_+'],
              normalize_tf_expression=True)

_images/single_sample_tutorial_125_0.png

[37]:

# To show between SOX10 and SOX5 (SOX5 targets and represses SOX10 regions)
plot_eRegulon(scplus_obj,
              reduction_name='eRegulons_UMAP',
              selected_regulons=['SOX10_+_+', 'SOX9_+_+', 'SOX5_-_+', 'SOX6_-_+'],
              normalize_tf_expression=True)

_images/single_sample_tutorial_126_0.png

[ ]:

import dill
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  dill.dump(scplus_obj, f)

D. eRegulon specificity scores

Recently, a novel method was developed and used to quantify the specificity of the regulons (from SCENIC) across different cell types (Suo et al., 2018). The RSS (Regulon Specificity Score) does not require binarization of a regulon’s enrichment score distribution and measures the distance between this distribution and the distribution of group annotations using the Jensen–Shannon Divergence. For a given group, the RSS for all predicted regulons is ranked from high to low, and highly group-specific regulons are spotted as outliers. This method can be applied to eRegulons as well.

[ ]:

import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()

[38]:

from scenicplus.RSS import *
regulon_specificity_scores(scplus_obj,
                         'ACC_Seurat_cell_type',
                         signature_keys=['Gene_based'],
                         selected_regulons=selected_cistromes['Cistrome'],
                         out_key_suffix='_gene_based',
                         scale=False)

[39]:

plot_rss(scplus_obj, 'ACC_Seurat_cell_type_gene_based', num_columns=4, top_n=10)

_images/single_sample_tutorial_132_0.png

[ ]:

import dill
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  dill.dump(scplus_obj, f)

E. Integrated multiome plot

We can also generate plots showing the chromatin profiles per group, region-to-gene relationships and TF and gene expression.

[ ]:

import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()

[40]:

# Generate interaction and annotation pyranges
import matplotlib.pyplot as plt
from scenicplus.utils import get_interaction_pr
import pyranges as pr
bigwig_dir = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/atac/pycistopic/consensus_peak_calling/seurat_pseudobulk_bw_files/'
bw_dict = {x.replace('.bw', ''): os.path.join(bigwig_dir, x) for x in os.listdir(bigwig_dir) if '.bw' in x}
pr_consensus_bed = pr.read_bed('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/atac/pycistopic/consensus_peak_calling/consensus_regions.bed')
pr_interact = get_interaction_pr(scplus_obj, 'hsapiens', 'hg38', inplace = False, subset_for_eRegulons_regions = True, eRegulons_key = 'eRegulons_importance')
gtf_file = "/lustre1/project/stg_00002/lcb/fderop/data/00000000_genomes/GRCh38_STAR_2.7.5_rna/genes.gtf"
pr_gtf = pr.read_gtf(gtf_file)

/opt/venv/lib/python3.8/site-packages/scenicplus/utils.py:655: DtypeWarning:

Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.

/opt/venv/lib/python3.8/site-packages/scenicplus/utils.py:712: RuntimeWarning:

invalid value encountered in true_divide

/opt/venv/lib/python3.8/site-packages/scenicplus/utils.py:723: RuntimeWarning:

invalid value encountered in true_divide

[70]:

# Plot
from importlib import reload
from scenicplus.plotting.coverageplot import *
fig = coverage_plot(
        SCENICPLUS_obj = scplus_obj,
        bw_dict = bw_dict,
        region = 'chr1:225800009-225860175',
        figsize = (10,20),
            pr_gtf = pr_gtf,
        color_dict = None,
        plot_order = None,
        pr_interact = pr_interact,
        genes = ['TMEM63A', 'SOX5'],
        meta_data_key = 'ACC_Seurat_cell_type',
        pr_consensus_bed = pr_consensus_bed,
        fontsize_dict={'bigwig_label': 12, 'gene_label': 0, 'violinplots_xlabel': 10, 'title': 12, 'bigwig_tick_label': 0, 'violinplots_ylabel': 3},
        height_ratios_dict = {'bigwig_violin': 1, 'genes': 0.5, 'arcs': 10, 'custom_ax': 5})
plt.tight_layout()

_images/single_sample_tutorial_138_0.png

[71]:

import dill
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  dill.dump(scplus_obj, f)

F. Adding DEGs and DARs

We can as well include DEGs and DARs in the SCENIC+ object. Both DEGs and DARs can be computed; however, we recommend to compute DARs with pycisTopic as it provides a more efficient implementation for big data sets (and pycisTopic DARs are already used for motif enrichment). Note that results can be slightly different due to filtering.

[1]:

import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()

[6]:

#from pyscenic.diff_features import *
get_differential_features(scplus_obj, 'ACC_VSN_cell_type', use_hvg = True, contrast_type = ['DARs', 'DEGs'])
get_differential_features(scplus_obj, 'ACC_Seurat_cell_type', use_hvg = True, contrast_type = ['DARs', 'DEGs'])

2022-01-05 19:49:40,968 SCENIC+      INFO     Calculating DARs for variable ACC_VSN_cell_type
2022-01-05 19:50:13,849 SCENIC+      INFO     There are 93456 variable features

/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.3' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.9' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.9' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_RNA+ATAC_leiden_100_2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_leiden_10_0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_leiden_10_1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.3' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_RNA+ATAC_leiden_100_2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_harmony_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_bbknn_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_scanorama_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_cca_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_1.5' as categorical

2022-01-05 19:51:25,668 SCENIC+      INFO     Finished calculating DARs for variable ACC_VSN_cell_type
2022-01-05 19:51:25,671 SCENIC+      INFO     Calculating DEGs for variable ACC_VSN_cell_type
2022-01-05 19:51:31,555 SCENIC+      INFO     There are 4134 variable features

/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.3' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.9' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.9' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_RNA+ATAC_leiden_100_2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_leiden_10_0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_leiden_10_1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.3' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_RNA+ATAC_leiden_100_2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_harmony_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_bbknn_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_scanorama_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_cca_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_1.5' as categorical

2022-01-05 19:51:34,598 SCENIC+      INFO     Finished calculating DEGs for variable ACC_VSN_cell_type
2022-01-05 19:51:34,600 SCENIC+      INFO     Calculating DARs for variable ACC_Seurat_cell_type
2022-01-05 19:52:08,736 SCENIC+      INFO     There are 101191 variable features

/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.3' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.9' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.9' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_RNA+ATAC_leiden_100_2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_leiden_10_0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_leiden_10_1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.3' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_RNA+ATAC_leiden_100_2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_harmony_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_bbknn_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_scanorama_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_cca_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_1.5' as categorical

2022-01-05 19:53:36,129 SCENIC+      INFO     Finished calculating DARs for variable ACC_Seurat_cell_type
2022-01-05 19:53:36,132 SCENIC+      INFO     Calculating DEGs for variable ACC_Seurat_cell_type
2022-01-05 19:53:37,694 SCENIC+      INFO     There are 4524 variable features

/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.3' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.9' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.9' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_RNA+ATAC_leiden_100_2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_leiden_10_0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_leiden_10_1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.3' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_RNA+ATAC_leiden_100_2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_harmony_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_bbknn_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_scanorama_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_cca_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_1.5' as categorical

2022-01-05 19:53:41,195 SCENIC+      INFO     Finished calculating DEGs for variable ACC_Seurat_cell_type

[18]:

import dill
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  dill.dump(scplus_obj, f)

G. Export to loom

You can also export the data to a loom file (gene based and region based, respectively). These loom files can be queried at https://scope.aertslab.org/.

[1]:

import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()

[3]:

from scenicplus.loom import *
export_to_loom(scplus_obj,
               signature_key = 'Gene_based',
               tree_structure = ('10x_multiome_brain', 'SCENIC+'),
               title = 'Tutorial - Gene based eGRN',
               nomenclature = "hg38",
               out_fname='/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/HC_gene_based.loom')

2022-01-06 17:37:54,023 SCENIC+      INFO     Formatting data
2022-01-06 17:38:01,938 SCENIC+      INFO     Creating minimal loom
2022-01-06 17:38:13,182 SCENIC+      INFO     Adding annotations
2022-01-06 17:38:16,020 SCENIC+      INFO     Adding clusterings
2022-01-06 17:38:16,375 SCENIC+      INFO     Adding markers
2022-01-06 17:38:17,649 SCENIC+      INFO     Exporting

/opt/venv/lib/python3.8/site-packages/loomxpy/loomxpy.py:459: FutureWarning: The default value of regex will change from True to False in a future version.
  regulons.columns = regulons.columns.str.replace("_?\\(", "_(")
/opt/venv/lib/python3.8/site-packages/loomxpy/loomxpy.py:437: FutureWarning: The default value of regex will change from True to False in a future version.
  regulons.columns = regulons.columns.str.replace("_?\\(", "_(")

[5]:

export_to_loom(scplus_obj,
               signature_key = 'Region_based',
               tree_structure = ('10x_multiome_brain', 'SCENIC+'),
               title = 'Tutorial - Gene based eGRN',
               nomenclature = "hg38",
               out_fname='/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/HC_region_based.loom')

2022-01-06 17:38:44,989 SCENIC+      INFO     Formatting data
2022-01-06 17:38:59,521 SCENIC+      INFO     Creating minimal loom
2022-01-06 17:43:59,040 SCENIC+      INFO     Adding annotations
2022-01-06 17:44:49,492 SCENIC+      INFO     Adding clusterings
2022-01-06 17:44:49,952 SCENIC+      INFO     Adding markers
2022-01-06 17:45:17,312 SCENIC+      INFO     Exporting

G. cisTarget on eRegulons

You can confirm the quality of the eRegulons by running cisTarget on them.

[12]:

import pyranges as pr
from pycistarget.utils import *
selected_regulons = ['SOX10_+_+', 'OLIG2_+_+']
selected_regulons = [list(filter(lambda x: x.startswith(y), scplus_obj.uns['eRegulon_signatures']['Region_based'].keys()))[0] for y in selected_regulons]
region_sets = {key: pr.PyRanges(region_names_to_coordinates(scplus_obj.uns['eRegulon_signatures']['Region_based'][key])) for key in selected_regulons}
selected_regulons

[12]:

['SOX10_+_+_(614r)', 'OLIG2_+_+_(172r)']

[13]:

# Load cistarget functions
from pycistarget.motif_enrichment_cistarget import *
# Preload db, you can also just provide the path to the db. Preloading the database is useful if you want to test different parameters.
# This will take some time depending on the size of your database
db = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/atac/pycistarget/dbs/human_brain.regions_vs_motifs.rankings.feather'
ctx_db = cisTargetDatabase(db, region_sets)
# Remove dbcorr motifs
ctx_db.db_rankings = ctx_db.db_rankings[~ctx_db.db_rankings.index.str.contains("dbcorr")]
# Run
cistarget_dict = run_cistarget(ctx_db = ctx_db,
                               region_sets = region_sets,
                               specie = 'homo_sapiens',
                               annotation_version = 'v9nr_clust',
                               path_to_motif_annotations = '/staging/leuven/stg_00002/lcb/cbravo/motif_clustering/RNA_harmony_snn_res_5_clusters/motif_collection_combined_motifs_stamp_and_singlets/hs_annotation.tsv',
                               auc_threshold = 0.005,
                               nes_threshold = 3.0,
                               rank_threshold = 0.05,
                               annotation = ['Direct_annot', 'Orthology_annot'],
                               n_cpu = 1,
                               _temp_dir='/scratch/leuven/313/vsc31305/ray_spill')

2022-01-06 17:53:14,926 cisTarget    INFO     Reading cisTarget database
2022-01-06 17:54:11,634 cisTarget    INFO     Running cisTarget for SOX10_+_+_(614r) which has 614 regions
2022-01-06 17:54:38,745 cisTarget    INFO     Annotating motifs for SOX10_+_+_(614r)
2022-01-06 17:54:40,659 cisTarget    INFO     Getting cistromes for SOX10_+_+_(614r)
2022-01-06 17:54:40,948 cisTarget    INFO     Running cisTarget for OLIG2_+_+_(172r) which has 172 regions
2022-01-06 17:54:46,872 cisTarget    INFO     Annotating motifs for OLIG2_+_+_(172r)
2022-01-06 17:54:49,364 cisTarget    INFO     Getting cistromes for OLIG2_+_+_(172r)
2022-01-06 17:54:49,890 cisTarget    INFO     Done!

[16]:

cistarget_results(cistarget_dict, name='SOX10_+_+_(614r)')

[16]:

	Region_set	Direct_annot	Orthology_annot	NES	AUC	Rank_at_max	Motif_hits
metacluster_46.3	SOX10_+_+_(614r)	SOX10, SOX8	NaN	14.464807	0.039040	21594.0	194
homer__CCATTGTTNY_Sox6	SOX10_+_+_(614r)	NaN	SOX6	13.828971	0.037454	21665.0	167
hocomoco__SOX9_HUMAN.H11MO.0.B	SOX10_+_+_(614r)	SOX9	NaN	11.155584	0.030785	21704.0	128
metacluster_46.1	SOX10_+_+_(614r)	HBP1, SOX13, SRY, SOX7, SOX10, SOX21, SOX30, SOX9, SOX2, SOX3, SOX15, SOX6, SOX4, SOX17, SOX1, SOX11, SOX18, SOX5, SOX8	SOX21, SOX17, SOX14	10.883511	0.030107	21232.0	204
taipale_cyt_meth__SOX14_NGAACAATGN_eDBD_meth	SOX10_+_+_(614r)	NaN	NaN	10.849951	0.030023	21490.0	147
metacluster_46.17	SOX10_+_+_(614r)	SOX10, SOX21, SOX6, SOX4, SOX12, SOX18, SRY, SMAD1, SOX14, SOX9, SOX5, SOX13, SOX2, SOX3, SOX15, SOX11, SOX8	SOX10, SOX6, SOX4, FOXJ3, SOX2, SOX3	10.448134	0.029021	21568.0	200
cisbp__M5208	SOX10_+_+_(614r)	NaN	SOX7, SOX17, SOX18	10.072985	0.028085	21647.0	157
transfac_pro__M08972	SOX10_+_+_(614r)	SOX17	NaN	9.528539	0.026727	21635.0	172
metacluster_46.18	SOX10_+_+_(614r)	SOX6, NANOG, SOX30, SRY, SOX18, SOX14, SOX9, SOX17, SOX13, SOX5, SOX2, SOX3, SOX15, SOX8	SOX21, SOX30, SOX12, SOX18, SRY, SOX14, SOX17, SOX2, NANOGP8, SOX7, SOX15, SOX11	9.178860	0.025855	21716.0	176
metacluster_46.2	SOX10_+_+_(614r)	SOX10, SOX7, SRY, SOX17	NaN	8.961321	0.025312	21788.0	161
swissregulon__hs__SOX2.p2	SOX10_+_+_(614r)	SOX2	NaN	8.960123	0.025309	21559.0	147
transfac_pro__M01308	SOX10_+_+_(614r)	SOX4	NaN	8.839967	0.025009	21618.0	156
factorbook__SOX2	SOX10_+_+_(614r)	SOX2	NaN	8.830678	0.024986	21593.0	144
jaspar__MA0515.1	SOX10_+_+_(614r)	NaN	SOX6	8.796220	0.024900	20253.0	145
cisbp__M5207	SOX10_+_+_(614r)	NaN	SOX4, SOX12, SOX11	8.790526	0.024886	21566.0	116
cisbp__M1904	SOX10_+_+_(614r)	SOX9	NaN	8.548118	0.024281	21785.0	170
metacluster_46.26	SOX10_+_+_(614r)	NaN	NaN	8.296420	0.023653	21589.0	106
taipale_cyt_meth__SOX14_CCGAACAATN_FL_meth	SOX10_+_+_(614r)	SOX14	NaN	7.757967	0.022310	20657.0	95
metacluster_2.93	SOX10_+_+_(614r)	SOX9, SOX17, SOX4, SOX18	NaN	7.619833	0.021966	21638.0	165
swissregulon__hs__SOX_8_9_10_.p2	SOX10_+_+_(614r)	SOX10, SOX9, SOX8	NaN	7.029543	0.020493	21716.0	168
cisbp__M1601	SOX10_+_+_(614r)	NaN	SOX11	6.980402	0.020371	21654.0	146
metacluster_1.73	SOX10_+_+_(614r)	SOX5, SOX6	NaN	6.814102	0.019956	21447.0	142
transfac_pro__M07338	SOX10_+_+_(614r)	SOX10	NaN	6.533939	0.019257	21758.0	185
transfac_pro__M07286	SOX10_+_+_(614r)	FOXO1	NaN	6.351158	0.018801	21762.0	73
hocomoco__SOX4_HUMAN.H11MO.0.B	SOX10_+_+_(614r)	SOX4	NaN	6.314602	0.018710	21406.0	141
metacluster_46.22	SOX10_+_+_(614r)	SOX17	SOX4, SOX30, SOX12, SOX5, SRY, SOX8	6.298721	0.018670	20652.0	144
metacluster_1.71	SOX10_+_+_(614r)	NaN	ZNF8	6.178865	0.018371	19245.0	65
hocomoco__SOX2_HUMAN.H11MO.0.A	SOX10_+_+_(614r)	SOX2	NaN	6.174371	0.018360	21633.0	138
swissregulon__hs__SRY.p2	SOX10_+_+_(614r)	SRY	NaN	6.127927	0.018244	20473.0	107
metacluster_61.1	SOX10_+_+_(614r)	POLR2A, SOX3	SOX14	6.032042	0.018005	20289.0	109
tfdimers__MD00293	SOX10_+_+_(614r)	SMAD1, SOX4	NaN	5.987995	0.017895	20532.0	122
metacluster_1.72	SOX10_+_+_(614r)	SRY	IRX3, IRX5, IRX1, IRX2, IRX4, IRX6	5.949341	0.017799	21253.0	102
metacluster_1.13	SOX10_+_+_(614r)	FOXC2, FOXD3, SOX4	NaN	5.763864	0.017336	12447.0	46
transfac_public__M00042	SOX10_+_+_(614r)	SOX5	NaN	5.641911	0.017032	21689.0	136
transfac_pro__M01630	SOX10_+_+_(614r)	NaN	FOXI2, FOXI3, FOXG1, FOXK1, FOXJ2, FOXF1, FOXJ3, FOXF2, FOXK2, FOXJ1, FOXL2, FOXI1, FOXQ1	5.585578	0.016891	19643.0	76
metacluster_46.21	SOX10_+_+_(614r)	SOX12, SOX14	NaN	5.553817	0.016812	21686.0	111
transfac_pro__M01216	SOX10_+_+_(614r)	FOXO1, FOXG1	NaN	5.346166	0.016294	18303.0	71
metacluster_49.30	SOX10_+_+_(614r)	SOX5	SOX5, SOX13	5.303318	0.016187	20082.0	116
transfac_public__M00410	SOX10_+_+_(614r)	SOX9	NaN	5.267960	0.016099	21727.0	143
jaspar__MA0143.3	SOX10_+_+_(614r)	NaN	NaN	5.261668	0.016083	21762.0	107
metacluster_22.14	SOX10_+_+_(614r)	NaN	IRX3, IRX5, DMRTA2, IRX1, IRX2, DMRTA1, IRX4, IRX6	5.132823	0.015762	8711.0	48
cisbp__M5110	SOX10_+_+_(614r)	NaN	IRX3, IRX5, IRX1, IRX2, IRX4, IRX6	5.125931	0.015745	12395.0	50
transfac_public__M00148	SOX10_+_+_(614r)	SRY	NaN	5.093869	0.015665	21362.0	76
metacluster_46.15	SOX10_+_+_(614r)	NANOG, TCF7L1, POU5F1, SOX2	NaN	5.032144	0.015511	20843.0	91
tfdimers__MD00291	SOX10_+_+_(614r)	YY2, FOXO1	NaN	5.028248	0.015501	21293.0	92
hdpi__BOLL	SOX10_+_+_(614r)	BOLL	NaN	5.013266	0.015464	21176.0	68
predrem__nrMotif1	SOX10_+_+_(614r)	NaN	NaN	4.799323	0.014930	19284.0	62
cisbp__M2502	SOX10_+_+_(614r)	NaN	NaN	4.735200	0.014770	10556.0	48
metacluster_23.20	SOX10_+_+_(614r)	SRY	METTL14	4.734601	0.014769	20641.0	69
metacluster_2.54	SOX10_+_+_(614r)	SOX10, DMRT1	DMRTB1, HBP1	4.696247	0.014673	21777.0	153
hocomoco__SOX2_HUMAN.H11MO.1.A	SOX10_+_+_(614r)	SOX2	NaN	4.683063	0.014640	20853.0	79
metacluster_46.8	SOX10_+_+_(614r)	SOX9, HNF4G, SOX15	SOX12, SOX11, SOX4	4.614446	0.014469	19952.0	103
taipale_tf_pairs__SOX6_CACCGAACAAT_HT	SOX10_+_+_(614r)	SOX6	NaN	4.542832	0.014290	15747.0	76
hocomoco__SOX9_MOUSE.H11MO.1.A	SOX10_+_+_(614r)	NaN	SOX9	4.542232	0.014289	21368.0	134
cisbp__M6017	SOX10_+_+_(614r)	NaN	FOXJ3	4.535341	0.014272	1646.0	15
metacluster_1.9	SOX10_+_+_(614r)	FOXO4, FOXP2, FOXO1	FOXI2, FOXI3, FOXG1, FOXK1, FOXJ2, FOXF1, FOXJ3, FOXF2, FOXO1, FOXJ1, FOXK2, FOXL2, FOXI1, FOXQ1	4.502380	0.014189	18273.0	65
hocomoco__FOXO4_HUMAN.H11MO.0.C	SOX10_+_+_(614r)	FOXO4	NaN	4.445149	0.014047	17178.0	63
metacluster_86.5	SOX10_+_+_(614r)	NR3C1, AR, PGR	AR, PGR	4.347466	0.013803	6196.0	32
taipale__SOX21_DBD_AACAATNNNNAKTGTT	SOX10_+_+_(614r)	SOX21	NaN	4.257574	0.013579	13472.0	52
transfac_pro__M02807	SOX10_+_+_(614r)	NaN	NaN	4.221618	0.013489	21720.0	109
cisbp__M1597	SOX10_+_+_(614r)	NaN	SOX14	4.205137	0.013448	21749.0	114
transfac_pro__M02907	SOX10_+_+_(614r)	NaN	SOX21	4.117043	0.013228	21626.0	113
metacluster_46.16	SOX10_+_+_(614r)	NaN	NaN	4.001083	0.012939	3326.0	20
metacluster_86.15	SOX10_+_+_(614r)	NR3C1, AR, PGR	NaN	3.976512	0.012878	1423.0	12
cisbp__M0749	SOX10_+_+_(614r)	NaN	FOXI2, FOXI3, FOXG1, FOXK1, FOXJ2, FOXF1, FOXJ3, FOXF2, FOXK2, FOXJ1, FOXL2, FOXI1, FOXQ1	3.920779	0.012739	21117.0	67
tfdimers__MD00591	SOX10_+_+_(614r)	SRY, TFAP2C	NaN	3.895609	0.012676	18111.0	78
metacluster_9.78	SOX10_+_+_(614r)	NaN	NaN	3.894411	0.012673	18643.0	66
transfac_pro__M01082	SOX10_+_+_(614r)	BRCA1, USF2	NaN	3.873736	0.012621	21629.0	76
metacluster_83.10	SOX10_+_+_(614r)	NaN	NaN	3.772158	0.012368	7670.0	30
metacluster_46.19	SOX10_+_+_(614r)	NaN	SOX5	3.770959	0.012365	21533.0	114
metacluster_48.24	SOX10_+_+_(614r)	HOXD10	NaN	3.753880	0.012322	1243.0	11
metacluster_6.40	SOX10_+_+_(614r)	TOB2	NaN	3.729010	0.012260	2119.0	13
transfac_pro__M01146	SOX10_+_+_(614r)	DMRT1	NaN	3.686161	0.012153	15072.0	53
cisbp__M2132	SOX10_+_+_(614r)	NaN	NaN	3.659194	0.012086	21175.0	71
predrem__nrMotif1554	SOX10_+_+_(614r)	NaN	NaN	3.600165	0.011939	8867.0	35
taipale_tf_pairs__SOX17_ACCGAACAAT_HT	SOX10_+_+_(614r)	SOX17	NaN	3.512969	0.011721	21229.0	88
flyfactorsurvey__BCl6-F5_CG4360F2-3_SOLEXA_2.5	SOX10_+_+_(614r)	NaN	NaN	3.510872	0.011716	1067.0	10
taipale_cyt_meth__SOX12_ACCGAACAATN_eDBD_meth	SOX10_+_+_(614r)	NaN	NaN	3.496190	0.011679	21730.0	99
cisbp__M0720	SOX10_+_+_(614r)	NaN	NaN	3.395810	0.011429	19112.0	55
metacluster_23.5	SOX10_+_+_(614r)	ABL1, BPTF, FOXP1, FUBP1	FOXP2, FOXL1, FOXP4, FOXP3, FOXP1, FOXM1	3.385323	0.011403	16948.0	57
metacluster_2.61	SOX10_+_+_(614r)	ACO1	NaN	3.363149	0.011348	1740.0	13
metacluster_14.62	SOX10_+_+_(614r)	NaN	NaN	3.348467	0.011311	1550.0	12
metacluster_23.3	SOX10_+_+_(614r)	ZIM3	NaN	3.312810	0.011222	21165.0	60
metacluster_14.21	SOX10_+_+_(614r)	SOX18	NaN	3.284644	0.011152	19148.0	68
metacluster_19.39	SOX10_+_+_(614r)	NNT	NaN	3.265467	0.011104	956.0	9
metacluster_46.10	SOX10_+_+_(614r)	POU5F1, CBX3, TAF7, SOX9, TCF12, NR3C1	NaN	3.264568	0.011102	21614.0	87
taipale_tf_pairs__HOXB2_SOX15_NYMATTANNNNNNACAATR_CAP_repr	SOX10_+_+_(614r)	HOXB2, SOX15	NaN	3.245091	0.011053	21491.0	79
metacluster_23.31	SOX10_+_+_(614r)	NaN	NaN	3.230709	0.011017	8906.0	34
cisbp__M6261	SOX10_+_+_(614r)	NR3C1	NaN	3.219922	0.010990	8191.0	30
metacluster_1.8	SOX10_+_+_(614r)	FOXP2, FOXO4, FOXP1, FOXO1, FOXO6, FOXO3, FOXQ1	FOXP2, FOXP1, FOXJ3, FOXO1, FOXJ1, FOXL2, FOXP4, FOXP3, FOXO3	3.210932	0.010968	21728.0	61
tfdimers__MD00100	SOX10_+_+_(614r)	SOX10, FOXO1, FOXG1	NaN	3.192055	0.010921	14149.0	44
metacluster_78.18	SOX10_+_+_(614r)	NaN	NaN	3.151004	0.010818	1909.0	12
predrem__nrMotif1377	SOX10_+_+_(614r)	NaN	NaN	3.131528	0.010770	11317.0	41
hocomoco__ANDR_HUMAN.H11MO.2.A	SOX10_+_+_(614r)	AR	NaN	3.123438	0.010750	20311.0	56
transfac_pro__M03193	SOX10_+_+_(614r)	NaN	NaN	3.118943	0.010738	1236.0	9
metacluster_46.35	SOX10_+_+_(614r)	NaN	NaN	3.096170	0.010682	21769.0	83
metacluster_1.11	SOX10_+_+_(614r)	FOXC1	NaN	3.059914	0.010591	1979.0	12
metacluster_49.24	SOX10_+_+_(614r)	NaN	NaN	3.053621	0.010575	613.0	7
cisbp__M6033	SOX10_+_+_(614r)	NaN	HOXD3	3.024257	0.010502	1388.0	10
metacluster_49.28	SOX10_+_+_(614r)	HOXC8, FOXD2	IRX3, FOXD2, IRX5, FOXD4L3, FOXD4L4, FOXD4L5, FOXD1, FOXD4L1, FOXD4L6, IRX1, FOXD3, IRX2, IRX4, IRX6, FOXD4	3.015567	0.010481	2587.0	13

H. Plotting networks

We can also plot networks using networkx and pyvis (for interactive networks). For example, here we are going to make a small network with 10 oligodendroycte genes controlled by SOX10, OLIG2 and TCF12.

[1]:

from scenicplus.networks import *
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()

The following function will generate a dictionary with node and edge features. It is possible to add additional columns to these data frames (e.g. enrichment of a motif in a region), to use it afterwards as variable to control the features of nodes and edges when plotting. Alternatively, we can also calculate logFC between given groups to use as variable as well.

[2]:

import networkx as nx
subset_genes = ['PLP1', 'SOX8', 'EPN1', 'OLIG1', 'MOG', 'OLIG2', 'SOX10', 'PLLP', 'PBX3', 'LRRN1', 'UBL8']
nx_tables = create_nx_tables(scplus_obj,
                    eRegulon_metadata_key = 'eRegulon_metadata',
                    subset_eRegulons = ['SOX10', 'OLIG2', 'TCF12'],
                    subset_regions = None,
                    subset_genes = subset_genes,
                    add_differential_gene_expression = True,
                    add_differential_region_accessibility = True,
                    differential_variable = ['ACC_Seurat_cell_type', 'ACC_VSN_cell_type'])

/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_cell_type' as categorical

We can now generate the network. To control features (e.g. size, color and transparency of nodes; width, color, transparency of edges, size and color of labels), we can pass dictionaries. Each dictitonary will be named by the slot on the tables dictionary and will include the name of the variable to use (‘variable’; if you want to use a fixed value use ‘fixed_VARNAME’), and scales (a dictionary if a discrete variable is used, a colormap if a continuous map is used, with minimum and maximum values). Two layouts are available: ‘concentrical’ and Kamada Kawai.

[3]:

from scenicplus.networks import *
G_c, pos_c, edge_tables_c, node_tables_c = create_nx_graph(nx_tables,
                   use_edge_tables = ['TF2R','R2G'],
                   color_edge_by = {'TF2R': {'variable' : 'TF', 'category_color' : {'SOX10': 'Orange', 'OLIG2': 'Purple', 'TCF12': 'Red'}},
                                    'R2G': {'variable' : 'R2G_rho', 'continuous_color' : 'viridis', 'v_min': -1, 'v_max': 1}},
                   transparency_edge_by =  {'R2G': {'variable' : 'R2G_importance', 'min_alpha': 0.1, 'v_min': 0}},
                   width_edge_by = {'R2G': {'variable' : 'R2G_importance', 'max_size' :  1.5, 'min_size' : 1}},
                   color_node_by = {'TF': {'variable': 'TF', 'category_color' : {'SOX10': 'Orange', 'OLIG2': 'Purple',  'TCF12': 'Red'}},
                                    'Gene': {'variable': 'ACC_Seurat_cell_type_Log2FC_MOL', 'continuous_color' : 'bwr'},
                                    'Region': {'variable': 'ACC_Seurat_cell_type_Log2FC_MOL', 'continuous_color' : 'viridis'}},
                   transparency_node_by =  {'Region': {'variable' : 'ACC_Seurat_cell_type_Log2FC_MOL', 'min_alpha': 0.1},
                                    'Gene': {'variable' : 'ACC_Seurat_cell_type_Log2FC_MOL', 'min_alpha': 0.1}},
                   size_node_by = {'TF': {'variable': 'fixed_size', 'fixed_size': 30},
                                    'Gene': {'variable': 'fixed_size', 'fixed_size': 15},
                                    'Region': {'variable': 'fixed_size', 'fixed_size': 10}},
                   shape_node_by = {'TF': {'variable': 'fixed_shape', 'fixed_shape': 'ellipse'},
                                    'Gene': {'variable': 'fixed_shape', 'fixed_shape': 'ellipse'},
                                    'Region': {'variable': 'fixed_shape', 'fixed_shape': 'diamond'}},
                   label_size_by = {'TF': {'variable': 'fixed_label_size', 'fixed_label_size': 20.0},
                                    'Gene': {'variable': 'fixed_label_size', 'fixed_label_size': 10.0},
                                    'Region': {'variable': 'fixed_label_size', 'fixed_label_size': 0.0}},
                   layout='concentrical_layout',
                   scale_position_by=250)

It is possible to plot static networks with networkx:

[4]:

# To draw with networkx
plt.figure(figsize=(10,7))
plot_networkx(G_c, pos_c)

_images/single_sample_tutorial_163_0.png

Or to generate an interactive visualization with pyvis. If opening the html file, you will see a menu to change options:

[5]:

# Visualize with pyvis
import os
os.chdir('/data/leuven/313/vsc31305/jupyterhub_notebooks/Multiomics_pipeline/10x_human_cerebellum/')
from pyvis.network import Network
nt = Network(notebook=True)
nt.from_nx(G_c)
nt.show_buttons()
nt.toggle_physics(False)
nt.show('olig_small_nx_c.html')

[5]:

[6]:

# To save as html
from pyvis.network import Network
nt = Network(notebook=False)
nt.from_nx(G_c)
nt.show_buttons(filter_=['physics'])
nt.toggle_physics(False)
nt.show('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/olig_small_nx_c.html')

We can also show the results using the Kamada Kawai algorithm:

[7]:

G_kk, pos_kk, edge_tables_kk, node_tables_kk = create_nx_graph(nx_tables,
                   use_edge_tables = ['TF2R','R2G'],
                   color_edge_by = {'TF2R': {'variable' : 'TF', 'category_color' : {'SOX10': 'Orange', 'OLIG2': 'Purple', 'TCF12': 'Red'}},
                                    'R2G': {'variable' : 'R2G_rho', 'continuous_color' : 'viridis', 'v_min': -1, 'v_max': 1}},
                   transparency_edge_by =  {'R2G': {'variable' : 'R2G_importance', 'min_alpha': 0.1, 'v_min': 0}},
                   width_edge_by = {'R2G': {'variable' : 'R2G_importance', 'max_size' :  1.5, 'min_size' : 1}},
                   color_node_by = {'TF': {'variable': 'TF', 'category_color' : {'SOX10': 'Orange', 'OLIG2': 'Purple',  'TCF12': 'Red'}},
                                    'Gene': {'variable': 'ACC_Seurat_cell_type_Log2FC_MOL', 'continuous_color' : 'bwr'},
                                    'Region': {'variable': 'ACC_Seurat_cell_type_Log2FC_MOL', 'continuous_color' : 'viridis'}},
                   transparency_node_by =  {'Region': {'variable' : 'ACC_Seurat_cell_type_Log2FC_MOL', 'min_alpha': 0.1},
                                    'Gene': {'variable' : 'ACC_Seurat_cell_type_Log2FC_MOL', 'min_alpha': 0.1}},
                   size_node_by = {'TF': {'variable': 'fixed_size', 'fixed_size': 30},
                                    'Gene': {'variable': 'fixed_size', 'fixed_size': 15},
                                    'Region': {'variable': 'fixed_size', 'fixed_size': 10}},
                   shape_node_by = {'TF': {'variable': 'fixed_shape', 'fixed_shape': 'ellipse'},
                                    'Gene': {'variable': 'fixed_shape', 'fixed_shape': 'ellipse'},
                                    'Region': {'variable': 'fixed_shape', 'fixed_shape': 'diamond'}},
                   label_size_by = {'TF': {'variable': 'fixed_label_size', 'fixed_label_size': 20.0},
                                    'Gene': {'variable': 'fixed_label_size', 'fixed_label_size': 10.0},
                                    'Region': {'variable': 'fixed_label_size', 'fixed_label_size': 0.0}},
                   layout='kamada_kawai_layout',
                   scale_position_by = 500)

[8]:

plt.figure(figsize=(10,10))
plot_networkx(G_kk, pos_kk)

_images/single_sample_tutorial_169_0.png

[9]:

# Visualize with pyvis
import os
os.chdir('/data/leuven/313/vsc31305/jupyterhub_notebooks/Multiomics_pipeline/10x_human_cerebellum/')
from pyvis.network import Network
nt = Network(notebook=True)
nt.from_nx(G_kk)
nt.show_buttons()
nt.toggle_physics(False)
nt.show('olig_small_nx_kk.html')

[9]:

[10]:

# To save as html
from pyvis.network import Network
nt = Network(notebook=False)
nt.from_nx(G_kk)
nt.show_buttons(filter_=['physics'])
nt.toggle_physics(False)
nt.show('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/olig_small_nx_kk.html')

I. Exporting to UCSC

While pseudobulk profiles can be generated with pycisTopic; we can export region eRegulons and region-to-gene links to visualize in UCSC.

[11]:

import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()

[12]:

from scenicplus.enhancer_to_gene import export_to_UCSC_interact
r2g_data = export_to_UCSC_interact(scplus_obj,
                            'hsapiens',
                            '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/human_cerebellum_r2g.rho.bed',
                            path_bedToBigBed='/staging/leuven/stg_00002/lcb/sdewin/PhD/De_Winter_hNTorg/COMBINED_ANALYSIS/r2g/',
                            bigbed_outfile='/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/human_cerebellum_r2g.rho.interact.bb',
                            region_to_gene_key='region_to_gene',
                            pbm_host='http://www.ensembl.org',
                            assembly='hg38',
                            ucsc_track_name='HC_R2G',
                            ucsc_description='Region_to_gene in the human cerebellum 10X data set',
                            cmap_neg='Reds',
                            cmap_pos='Greens',
                            key_for_color='rho',
                            vmin=-1,
                            vmax=1,
                            scale_by_gene=False,
                            subset_for_eRegulons_regions=True,
                            eRegulons_key='eRegulons_importance')

2022-01-11 20:02:09,140 R2G          INFO     Downloading gene annotation from biomart, using dataset: hsapiens_gene_ensembl
2022-01-11 20:02:09,742 R2G          INFO     Formatting data ...

/opt/venv/lib/python3.8/site-packages/scenicplus/enhancer_to_gene.py:750: DtypeWarning: Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.
  annot = dataset.query(attributes=['chromosome_name', 'start_position', 'end_position',

2022-01-11 20:02:30,715 R2G          INFO     Writing data to: /staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/human_cerebellum_r2g.rho.bed
2022-01-11 20:02:31,849 R2G          INFO     Writing data to: /staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/human_cerebellum_r2g.rho.interact.bb

[3]:

from scenicplus.utils import export_eRegulons
regions = export_eRegulons(scplus_obj,
                '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/human_cerebellum_eRegulons.bed',
                'hg38',
                bigbed_outfile = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/human_cerebellum_eRegulons.bb',
                eRegulon_metadata_key = 'eRegulon_metadata',
                eRegulon_signature_key = 'eRegulon_signatures',
                path_bedToBigBed='/staging/leuven/stg_00002/lcb/sdewin/PhD/De_Winter_hNTorg/COMBINED_ANALYSIS/r2g/')